14.2.6.1 Continunal Learning, Incremental Learning, Active Learning

Hong, Y.[Yi], Kwong, S.[Sam], Chang, Y.C.[Yu-Chou], Ren, Q.S.[Qing-Sheng],
Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm,
PR(41), No. 9, September 2008, pp. 2742-2756.
Elsevier DOI 0806
Clustering ensembles; Dimensionality unbiased; Population based incremental learning algorithm; Unsupervised feature selection BibRef

Lughofer, E.[Edwin],
Extensions of vector quantization for incremental clustering,
PR(41), No. 3, March 2008, pp. 995-1011.
Elsevier DOI 0711
Vector quantization; Clustering; Incremental learning; New winning cluster selection strategy; Removing cluster satellites; Split-and-merge strategy; Image classification framework; Fault detection; Evolving fuzzy models BibRef

Jia, P.[Peng], Yin, J.S.[Jun-Song], Huang, X.S.[Xin-Sheng], Hu, D.[Dewen],
Incremental Laplacian eigenmaps by preserving adjacent information between data points,
PRL(30), No. 16, 1 December 2009, pp. 1457-1463.
Elsevier DOI 0911
Laplacian eigenmaps; Incremental learning; Locally linear construction; Nonlinear dimensionality reduction BibRef

Li, H.S.[Hou-Sen], Jiang, H.[Hao], Barrio, R.[Roberto], Liao, X.K.[Xiang-Ke], Cheng, L.Z.[Li-Zhi], Su, F.[Fang],
Incremental manifold learning by spectral embedding methods,
PRL(32), No. 10, 15 July 2011, pp. 1447-1455.
Elsevier DOI 1106
Manifold learning; Incremental learning; Dimensionality reduction; Spectral embedding methods; Hessian eigenmaps BibRef

Lu, G.F.[Gui-Fu], Jian, Z.[Zou], Wang, Y.[Yong],
Incremental learning from chunk data for IDR/QR,
IVC(36), No. 1, 2015, pp. 1-8.
Elsevier DOI 1504
Feature extraction incremental dimension reduction. BibRef

Le, T.B.[Thanh-Binh], Kim, S.W.[Sang-Woon],
On incrementally using a small portion of strong unlabeled data for semi-supervised learning algorithms,
PRL(41), No. 1, 2014, pp. 53-64.
Elsevier DOI 1403
Semi-supervised learning BibRef

Zhang, Z., Li, Y., Zhang, Z., Jin, C., Gao, M.,
Adaptive Matrix Sketching and Clustering for Semisupervised Incremental Learning,
SPLetters(25), No. 7, July 2018, pp. 1069-1073.
IEEE DOI 1807
learning (artificial intelligence), matrix algebra, pattern classification, adaptive matrix sketching, semisupervised classification BibRef

Li, Y.C.[Yan-Chao], Wang, Y.L.[Yong-Li], Liu, Q.[Qi], Bi, C.[Cheng], Jiang, X.H.[Xiao-Hui], Sun, S.R.[Shu-Rong],
Incremental semi-supervised learning on streaming data,
PR(88), 2019, pp. 383-396.
Elsevier DOI 1901
Semi-supervised learning, Dynamic feature learning, Streaming data, Classification BibRef

Besedin, A.[Andrey], Blanchart, P.[Pierre], Crucianu, M.[Michel], Ferecatu, M.[Marin],
Deep online classification using pseudo-generative models,
CVIU(201), 2020, pp. 103048.
Elsevier DOI 2011
Avoid issues of forgetting. Deep learning, Online learning, Pseudo-generative models, Stream learning BibRef

Peng, C.[Can], Zhao, K.[Kun], Lovell, B.C.[Brian C.],
Faster ILOD: Incremental learning for object detectors based on faster RCNN,
PRL(140), 2020, pp. 109-115.
Elsevier DOI 2012
Deep learning, Object detection, Incremental learning BibRef

Wang, G.X.[Guang-Xing], Ren, P.[Peng],
Hyperspectral Image Classification with Feature-Oriented Adversarial Active Learning,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Xiang, S.C.[Sun-Cheng], Fu, Y.Z.[Yu-Zhuo], Liu, T.[Ting],
Progressive learning with style transfer for distant domain adaptation,
IET-IPR(14), No. 14, December 2020, pp. 3527-3535.
DOI Link 2012
BibRef

Berger, K.[Katja], Caicedo, J.P.R.[Juan Pablo Rivera], Martino, L.[Luca], Wocher, M.[Matthias], Hank, T.[Tobias], Verrelst, J.[Jochem],
A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
Survey, Active Learning. BibRef

Li, J.[Jia], Song, Y.F.[Ya-Fei], Zhu, J.F.[Jian-Feng], Cheng, L.L.[Le-Le], Su, Y.[Ying], Ye, L.[Lin], Yuan, P.C.[Peng-Cheng], Han, S.M.[Shu-Min],
Learning From Large-Scale Noisy Web Data With Ubiquitous Reweighting for Image Classification,
PAMI(43), No. 5, May 2021, pp. 1808-1814.
IEEE DOI 2104
Noise measurement, Deep learning, Task analysis, Training, Annotations, Solid modeling, Visualization, Image classification, deep learning BibRef

Gweon, H.[Hyukjun], Yu, H.[Hao],
A nearest neighbor-based active learning method and its application to time series classification,
PRL(146), 2021, pp. 230-236.
Elsevier DOI 2105
Active learning, Batch mode, Time series classification, Nearest neighbor BibRef

Wang, Y.[Yi], Ding, Y.[Yi], He, X.J.[Xiang-Jian], Fan, X.[Xin], Lin, C.[Chi], Li, F.Q.[Feng-Qi], Wang, T.Z.[Tian-Zhu], Luo, Z.X.[Zhong-Xuan], Luo, J.B.[Jie-Bo],
Novelty Detection and Online Learning for Chunk Data Streams,
PAMI(43), No. 7, July 2021, pp. 2400-2412.
IEEE DOI 2106
Kernel, Data models, Linear systems, Fans, Hilbert space, Streaming media, Feature extraction, Data stream, online learning BibRef

Fonseca, J.[Joao], Douzas, G.[Georgios], Bacao, F.[Fernando],
Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Celik, B.[Bilge], Vanschoren, J.[Joaquin],
Adaptation Strategies for Automated Machine Learning on Evolving Data,
PAMI(43), No. 9, September 2021, pp. 3067-3078.
IEEE DOI 2108
Pipelines, Adaptation models, Machine learning, Optimization, Data models, Task analysis, Bayes methods, AutoML, data streams, adaptation strategies BibRef

Zheng, X.[Xiawu], Zhang, Y.[Yang], Hong, S.[Sirui], Li, H.X.[Hui-Xia], Tang, L.[Lang], Xiong, Y.[Youcheng], Zhou, J.[Jin], Wang, Y.[Yan], Sun, X.S.[Xiao-Shuai], Zhu, P.F.[Peng-Fei], Wu, C.[Chenglin], Ji, R.R.[Rong-Rong],
Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors,
PAMI(43), No. 9, September 2021, pp. 3091-3107.
IEEE DOI 2108
Pipelines, Task analysis, Optimization, Data models, Computational modeling, Training, Search problems, evolutionary algorithm BibRef

Wang, K.[Kai], van de Weijer, J.[Joost], Herranz, L.[Luis],
ACAE-REMIND for online continual learning with compressed feature replay,
PRL(150), 2021, pp. 122-129.
Elsevier DOI 2109
online continual learning, autoencoders, vector quantization BibRef

Agarwal, M.[Mridul], Aggarwal, V.[Vaneet],
Blind decision making: Reinforcement learning with delayed observations,
PRL(150), 2021, pp. 176-182.
Elsevier DOI 2109
BibRef

Grigoletto, R.[Riccardo], Maiettini, E.[Elisa], Natale, L.[Lorenzo],
Score to Learn: A Comparative Analysis of Scoring Functions for Active Learning in Robotics,
CVS21(55-67).
Springer DOI 2109
BibRef

Dong, J.H.[Jia-Hua], Cong, Y.[Yang], Sun, G.[Gan], Zhang, T.[Tao],
Lifelong robotic visual-tactile perception learning,
PR(121), 2022, pp. 108176.
Elsevier DOI 2109
Lifelong machine learning, Robotics, Visual-tactile perception, Cross-modality learning, Multi-task learning BibRef

Liu, Y.Y.[Yu-Yang], Cong, Y.[Yang], Sun, G.[Gan], Ding, Z.M.[Zheng-Ming],
Lifelong Visual-Tactile Spectral Clustering for Robotic Object Perception,
CirSysVideo(33), No. 2, February 2023, pp. 818-829.
IEEE DOI 2302
Task analysis, Robots, Libraries, Manifolds, Correlation, Computational modeling, Visualization, Lifelong learning, modality-consistent and modality-invariant BibRef

Huang, F.X.[Fu-Xian], Li, W.C.[Wei-Chao], Cui, J.B.[Jia-Bao], Fu, Y.J.[Yong-Jian], Li, X.[Xi],
Unified curiosity-Driven learning with smoothed intrinsic reward estimation,
PR(123), 2022, pp. 108352.
Elsevier DOI 2112
Reinforcement learning, Unified curiosity-driven exploration, Robust intrinsic reward, Task-relevant feature BibRef

Yang, Y.[Yang], Chen, B.[Bo], Liu, H.W.[Hong-Wei],
Bayesian compression for dynamically expandable networks,
PR(122), 2022, pp. 108260.
Elsevier DOI 2112
Bayesian compression, DEN, Continual learning, Selective retraining, Dynamically expands network, Semantic drift BibRef

Wang, X.M.[Xiu-Mei], Guo, D.N.[Ding-Ning], Cheng, P.T.[Pei-Tao],
Support structure representation learning for sequential data clustering,
PR(122), 2022, pp. 108326.
Elsevier DOI 2112
Sequential data, Clustering, Support structure representation BibRef

Li, C.S.[Chang-Sheng], Li, R.Q.[Rong-Qing], Yuan, Y.[Ye], Wang, G.[Guoren], Xu, D.[Dong],
Deep Unsupervised Active Learning via Matrix Sketching,
IP(30), 2021, pp. 9280-9293.
IEEE DOI 2112
Image reconstruction, Image processing, Data models, Task analysis, Learning systems, Kernel, Manifolds, Unsupervised active learning, data reconstruction BibRef

Lomonaco, V.[Vincenzo], Pellegrini, L.[Lorenzo], Rodriguez, P.[Pau], Caccia, M.[Massimo], She, Q.[Qi], Chen, Y.[Yu], Jodelet, Q.[Quentin], Wang, R.P.[Rui-Ping], Mai, Z.[Zheda], Vazquez, D.[David], Parisi, G.I.[German I.], Churamani, N.[Nikhil], Pickett, M.[Marc], Laradji, I.[Issam], Maltoni, D.[Davide],
CVPR 2020 continual learning in computer vision competition: Approaches, results, current challenges and future directions,
AI(303), 2022, pp. 103635.
Elsevier DOI 2201
Continual learning, Lifelong learning, Incremental learning, Challenge BibRef

Zhou, S.[Shiji], Wang, L.[Lianzhe], Zhang, S.H.[Shang-Hang], Wang, Z.[Zhi], Zhu, W.[Wenwu],
Active Gradual Domain Adaptation: Dataset and Approach,
MultMed(24), 2022, pp. 1210-1220.
IEEE DOI 2203
Adaptation models, Uncertainty, Data models, Diversity reception, Deep learning, Performance evaluation, Internet, web noise data BibRef

Wu, D.R.[Dong-Rui], Huang, J.[Jian],
Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression,
AffCom(13), No. 1, January 2022, pp. 16-27.
IEEE DOI 2203
Task analysis, Affective computing, Estimation, Labeling, Computational modeling, Training, Active learning, greedy sampling BibRef

Li, C.S.[Chang-Sheng], Ma, H.D.[Han-Dong], Yuan, Y.[Ye], Wang, G.[Guoren], Xu, D.[Dong],
Structure Guided Deep Neural Network for Unsupervised Active Learning,
IP(31), No. 2022, pp. 2767-2781.
IEEE DOI 2204
Data models, Kernel, Task analysis, Image reconstruction, Training, Manifolds, Deep learning, Unsupervised active learning, imbalance data BibRef

Wang, J.[Jiao], Zhang, L.[Lemin], He, Z.Q.[Zhi-Qiang], Zhu, C.[Can], Zhao, Z.[Zihui],
Erlang planning network: An iterative model-based reinforcement learning with multi-perspective,
PR(128), 2022, pp. 108668.
Elsevier DOI 2205
Model-based reinforcement learning, Multi-perspective, Bi-level, Planning, Trajectory imagination BibRef

Korycki, L.[Lukasz], Krawczyk, B.[Bartosz],
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams,
PR(129), 2022, pp. 108749.
Elsevier DOI 2206
BibRef
Earlier:
Class-Incremental Experience Replay for Continual Learning under Concept Drift,
OmniCV21(3644-3653)
IEEE DOI 2109
Machine learning, Data stream mining, Concept drift, Sparse labeling, Active learning. Machine learning, Data mining, Task analysis BibRef

de Lange, M.[Matthias], Aljundi, R.[Rahaf], Masana, M.[Marc], Parisot, S.[Sarah], Jia, X.[Xu], Leonardis, A.[Aleš], Slabaugh, G.[Gregory], Tuytelaars, T.[Tinne],
A Continual Learning Survey: Defying Forgetting in Classification Tasks,
PAMI(44), No. 7, July 2022, pp. 3366-3385.
IEEE DOI 2206
Survey, Continual Learning. Task analysis, Knowledge engineering, Neural networks, Training, Training data, Learning systems, Interference, Continual learning, neural networks BibRef

Shen, Y.[Yeji], Song, Y.H.[Yu-Hang], Wu, C.H.[Chi-Hao], Kuo, C.C.J.[C.C. Jay],
TBAL: Two-stage batch-mode active learning for image classification,
SP:IC(106), 2022, pp. 116731.
Elsevier DOI 2206
Active learning, Image classification, Semi-supervised learning BibRef

Lan, C.L.[Chuan-Lin], Feng, F.[Fan], Liu, Q.[Qi], She, Q.[Qi], Yang, Q.[Qihan], Hao, X.Y.[Xin-Yue], Mashkin, I.[Ivan], Kei, K.S.[Ka Shun], Qiang, D.[Dong], Lomonaco, V.[Vincenzo], Shi, X.S.[Xue-Song], Wang, Z.W.[Zheng-Wei], Guo, Y.[Yao], Zhang, Y.M.[Yi-Min], Qiao, F.[Fei], Chan, R.H.M.[Rosa H.M.],
Towards lifelong object recognition: A dataset and benchmark,
PR(130), 2022, pp. 108819.
Elsevier DOI 2206
Robotic vision, Continual learning, Lifelong learning, Object recognition BibRef

Yang, Y.Z.[Ya-Zhou], Loog, M.[Marco],
To Actively Initialize Active Learning,
PR(131), 2022, pp. 108836.
Elsevier DOI 2208
active learning, active initialization, nearest neighbor criterion, minimum nearest neighbor distance BibRef

Koçanaogullari, A.[Aziz], Akcakaya, M.[Murat], Erdogmus, D.[Deniz],
Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry,
PAMI(44), No. 9, September 2022, pp. 5590-5601.
IEEE DOI 2208
Uncertainty, Entropy, Bayes methods, Geometry, Radar tracking, Probability distribution, Brain modeling, Active learning, optimal stopping criterion design BibRef

Li, M.[Min], Huang, T.Y.[Tian-Yi], Zhu, W.[William],
Clustering experience replay for the effective exploitation in reinforcement learning,
PR(131), 2022, pp. 108875.
Elsevier DOI 2208
Reinforcement learning, Clustering, Experience replay, Exploitation efficiency, Time division BibRef

He, C.[Chen], Wang, R.P.[Rui-Ping], Chen, X.L.[Xi-Lin],
Rethinking class orders and transferability in class incremental learning,
PRL(161), 2022, pp. 67-73.
Elsevier DOI 2209
Transferability, Class incremental learning, Class order BibRef

Tosatto, S.[Samuele], Carvalho, J.[João], Peters, J.[Jan],
Batch Reinforcement Learning With a Nonparametric Off-Policy Policy Gradient,
PAMI(44), No. 10, October 2022, pp. 5996-6010.
IEEE DOI 2209
Mathematical model, Estimation, Kernel, Reinforcement learning, Monte Carlo methods, Task analysis, Closed-form solutions, nonparametric estimation BibRef

Akrour, R.[Riad], Tateo, D.[Davide], Peters, J.[Jan],
Continuous Action Reinforcement Learning From a Mixture of Interpretable Experts,
PAMI(44), No. 10, October 2022, pp. 6795-6806.
IEEE DOI 2209
Task analysis, Complexity theory, Approximation algorithms, Neural networks, Trajectory, Reinforcement learning, robotics BibRef

Xu, J.[Ju], Ma, J.[Jin], Gao, X.S.[Xue-Song], Zhu, Z.X.[Zhan-Xing],
Adaptive Progressive Continual Learning,
PAMI(44), No. 10, October 2022, pp. 6715-6728.
IEEE DOI 2209
Task analysis, Optimization, Bayes methods, Training, Reinforcement learning, Knowledge engineering, Complexity theory, neural networks BibRef

Zhuang, C.[Chen], Huang, S.L.[Shao-Li], Cheng, G.[Gong], Ning, J.F.[Ji-Feng],
Multi-criteria Selection of Rehearsal Samples for Continual Learning,
PR(132), 2022, pp. 108907.
Elsevier DOI 2209
Continual Learning, Multiple Criteria, Rehersal Method, Learning to learn BibRef

Wan, Y.Y.[Yuan-Yu], Zhang, L.J.[Li-Jun],
Efficient Adaptive Online Learning via Frequent Directions,
PAMI(44), No. 10, October 2022, pp. 6910-6923.
IEEE DOI 2209
Complexity theory, Time complexity, Optimization, Mirrors, Approximation algorithms, Symmetric matrices, Transforms, adaptive subgradient methods BibRef

Zhang, J.[Ji], Song, J.K.[Jing-Kuan], Gao, L.[Lianli], Liu, Y.[Ye], Shen, H.T.[Heng Tao],
Progressive Meta-Learning With Curriculum,
CirSysVideo(32), No. 9, September 2022, pp. 5916-5930.
IEEE DOI 2209
Task analysis, Training, Adaptation models, Computational modeling, Ear, Standards, Pediatrics, Few-shot learning, meta-learning, hard task-sampling BibRef

Zhao, X.Y.[Xing-Yu], An, Y.X.[Yue-Xuan], Xu, N.[Ning], Geng, X.[Xin],
Continuous label distribution learning,
PR(133), 2023, pp. 109056.
Elsevier DOI 2210
Label distribution learning, Continuous label distribution, Label ambiguity, Label encoding, Label correlations BibRef

Zhang, M.Y.[Meng-Yang], Tian, G.H.[Guo-Hui], Gao, H.B.[Huan-Bing], Zhang, Y.[Ying],
Autonomous Generation of Service Strategy for Household Tasks: A Progressive Learning Method With A Priori Knowledge and Reinforcement Learning,
CirSysVideo(32), No. 11, November 2022, pp. 7473-7488.
IEEE DOI 2211
Correlation, Task analysis, Reinforcement learning, Artificial neural networks, reinforcement learning BibRef

Chen, X.[Xu], Wujek, B.[Brett],
A Unified Framework for Automatic Distributed Active Learning,
PAMI(44), No. 12, December 2022, pp. 9774-9786.
IEEE DOI 2212
Optimization, Semisupervised learning, Machine learning, Distributed databases, Big Data, Search problems, active learning BibRef

Jodelet, Q.[Quentin], Liu, X.[Xin], Murata, T.[Tsuyoshi],
Balanced softmax cross-entropy for incremental learning with and without memory,
CVIU(225), 2022, pp. 103582.
Elsevier DOI 2212
Continual learning, Class incremental learning, Image classification, Bias mitigation BibRef

Yu, H.[Hang], Liu, W.[Weixu], Lu, J.[Jie], Wen, Y.M.[Yi-Min], Luo, X.F.[Xiang-Feng], Zhang, G.Q.[Guang-Quan],
Detecting group concept drift from multiple data streams,
PR(134), 2023, pp. 109113.
Elsevier DOI 2212
Concept drift, Data streams, Online learning, Hypothesis test BibRef

Thuseethan, S.[Selvarajah], Rajasegarar, S.[Sutharshan], Yearwood, J.[John],
Deep Continual Learning for Emerging Emotion Recognition,
MultMed(24), 2022, pp. 4367-4380.
IEEE DOI 2212
Emotion recognition, Task analysis, Feature extraction, Learning systems, Transfer learning, Training, Databases, unknown emotions BibRef

Yang, H.Z.[Hong-Zheng], Chen, C.[Cheng], Jiang, M.[Meirui], Liu, Q.[Quande], Cao, J.F.[Jian-Feng], Heng, P.A.[Pheng Ann], Dou, Q.[Qi],
DLTTA: Dynamic Learning Rate for Test-Time Adaptation on Cross-Domain Medical Images,
MedImg(41), No. 12, December 2022, pp. 3575-3586.
IEEE DOI 2212
Adaptation models, Data models, Training, Predictive models, Training data, Computational modeling, Task analysis, dynamic learning rate BibRef

Hu, Q.H.[Qing-Hua], Gao, Y.C.[Yu-Cong], Cao, B.[Bing],
Curiosity-Driven Class-Incremental Learning via Adaptive Sample Selection,
CirSysVideo(32), No. 12, December 2022, pp. 8660-8673.
IEEE DOI 2212
Task analysis, Adaptation models, Knowledge engineering, Data models, Uncertainty, Training, Computational modeling, novelty BibRef

Ji, Z.[Zhong], Li, J.[Jin], Wang, Q.[Qiang], Zhang, Z.F.[Zhong-Fei],
Complementary Calibration: Boosting General Continual Learning With Collaborative Distillation and Self-Supervision,
IP(32), 2023, pp. 657-667.
IEEE DOI 2301
Task analysis, Feature extraction, Calibration, Collaboration, Training, Testing, Ear, General continual learning, supervised contrastive learning BibRef

Ji, Z.[Zhong], Hou, Z.[Zhishen], Liu, X.[Xiyao], Pang, Y.W.[Yan-Wei], Li, X.L.[Xue-Long],
Memorizing Complementation Network for Few-Shot Class-Incremental Learning,
IP(32), 2023, pp. 937-948.
IEEE DOI 2301
Task analysis, Power capacitors, Ensemble learning, Knowledge engineering, Feature extraction, Adaptation models, memorizing complementation BibRef

Yang, B.[Boyu], Lin, M.B.[Ming-Bao], Zhang, Y.X.[Yun-Xiao], Liu, B.H.[Bing-Hao], Liang, X.D.[Xiao-Dan], Ji, R.R.[Rong-Rong], Ye, Q.X.[Qi-Xiang],
Dynamic Support Network for Few-Shot Class Incremental Learning,
PAMI(45), No. 3, March 2023, pp. 2945-2951.
IEEE DOI 2302
Power capacitors, Training, Feature extraction, Adaptation models, Task analysis, Generators, Data models, support network BibRef

Bing, Z.S.[Zhen-Shan], Lerch, D.[David], Huang, K.[Kai], Knoll, A.[Alois],
Meta-Reinforcement Learning in Non-Stationary and Dynamic Environments,
PAMI(45), No. 3, March 2023, pp. 3476-3491.
IEEE DOI 2302
Task analysis, Training, Robots, Adaptation models, Multitasking, Inference algorithms, Gaussian mixture model, robotic control BibRef

Fu, Z.L.[Zhi-Ling], Wang, Z.[Zhe], Xu, X.L.[Xin-Lei], Li, D.D.[Dong-Dong], Yang, H.[Hai],
Knowledge aggregation networks for class incremental learning,
PR(137), 2023, pp. 109310.
Elsevier DOI 2302
Class incremental learning, Catastrophic forgetting, Dual-branch network, Knowledge aggregation, Model compression BibRef

Zamorski, M.[Maciej], Stypulkowski, M.[Michal], Karanowski, K.[Konrad], Trzcinski, T.[Tomasz], Zieba, M.[Maciej],
Continual learning on 3D point clouds with random compressed rehearsal,
CVIU(228), 2023, pp. 103621.
Elsevier DOI 2302
Continual learning, Point cloud, Deep learning, Data compression BibRef

Du, P.[Pan], Chen, H.[Hui], Zhao, S.[Suyun], Chai, S.[Shuwen], Chen, H.[Hong], Li, C.P.[Cui-Ping],
Contrastive Active Learning Under Class Distribution Mismatch,
PAMI(45), No. 4, April 2023, pp. 4260-4273.
IEEE DOI 2303
BibRef
Earlier: A1, A3, A2, A4, A5, A6:
Contrastive Coding for Active Learning under Class Distribution Mismatch,
ICCV21(8907-8916)
IEEE DOI 2203
Semantics, Dogs, Annotations, Data models, Costs, Task analysis, Supervised learning, Active learning, machine learning. Costs, Upper bound, Annotations, Text categorization, Representation learning BibRef

Zhang, X.[Xikun], Song, D.J.[Dong-Jin], Tao, D.C.[Da-Cheng],
Hierarchical Prototype Networks for Continual Graph Representation Learning,
PAMI(45), No. 4, April 2023, pp. 4622-4636.
IEEE DOI 2303
Task analysis, Prototypes, Feature extraction, Memory management, Knowledge engineering, Representation learning, graph neural networks BibRef

Martins, V.E.[Vinicius Eiji], Cano, A.[Alberto], Barbon Junior, S.[Sylvio],
Meta-learning for dynamic tuning of active learning on stream classification,
PR(138), 2023, pp. 109359.
Elsevier DOI 2303
Meta-learning, Active learning, Data stream, Concept drift BibRef

Mahapatra, D.[Dwarikanath], Poellinger, A.[Alexander], Reyes, M.[Mauricio],
Graph Node Based Interpretability Guided Sample Selection for Active Learning,
MedImg(42), No. 3, March 2023, pp. 661-673.
IEEE DOI 2303
Uncertainty, Measurement, Computational modeling, X-ray imaging, Entropy, Predictive models, Estimation, Interpretability, sample selection lung disease classification BibRef

Zhou, S.[Shiji], Wang, Z.[Zhi], Hu, C.H.[Cheng-Hao], Mao, Y.[Yinan], Yan, H.P.[Hao-Peng], Zhang, S.H.[Shang-Hang], Wu, C.[Chuan], Zhu, W.[Wenwu],
Caching in Dynamic Environments: A Near-Optimal Online Learning Approach,
MultMed(25), 2023, pp. 792-804.
IEEE DOI 2303
Heuristic algorithms, Streaming media, Size measurement, Reinforcement learning, Proposals, Optimization, Area measurement, online learning BibRef

Boschini, M.[Matteo], Bonicelli, L.[Lorenzo], Buzzega, P.[Pietro], Porrello, A.[Angelo], Calderara, S.[Simone],
Class-Incremental Continual Learning Into the eXtended DER-Verse,
PAMI(45), No. 5, May 2023, pp. 5497-5512.
IEEE DOI 2304
Task analysis, Training, Proposals, Data models, Optimization, Knowledge engineering, Interference, Continual learning, replay methods BibRef

Hu, H.[Hexiang], Sener, O.[Ozan], Sha, F.[Fei], Koltun, V.[Vladlen],
Drinking From a Firehose: Continual Learning With Web-Scale Natural Language,
PAMI(45), No. 5, May 2023, pp. 5684-5696.
IEEE DOI 2304
Task analysis, Benchmark testing, Learning systems, Multitasking, Social networking (online), Neural networks, Data models, web-scale datasets BibRef

Guo, J.F.[Ji-Feng], Pang, Z.Q.[Zhi-Qi], Bai, M.Y.[Miao-Yuan], Xiao, Y.B.[Yan-Bang], Zhang, J.[Jian],
Independency-enhancing adversarial active learning,
IET-IPR(17), No. 5, 2023, pp. 1427-1437.
DOI Link 2304
image classification, image segmentation BibRef

Lin, H.[Huiwei], Feng, S.S.[Shan-Shan], Li, X.[Xutao], Li, W.T.[Wen-Tao], Ye, Y.M.[Yun-Ming],
Anchor Assisted Experience Replay for Online Class-Incremental Learning,
CirSysVideo(33), No. 5, May 2023, pp. 2217-2232.
IEEE DOI 2305
Automobiles, Airplanes, Task analysis, Atmospheric modeling, Training, Reservoirs, Memory management, image recognition BibRef

Hedegaard, L.[Lukas], Iosifidis, A.[Alexandros],
Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in Pytorch,
CADK22(21-34).
Springer DOI 2304
BibRef

Osowiechi, D.[David], Hakim, G.A.V.[Gustavo A. Vargas], Noori, M.[Mehrdad], Cheraghalikhani, M.[Milad], Ben Ayed, I.[Ismail], Desrosiers, C.[Christian],
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow,
WACV23(2125-2126)
IEEE DOI 2302
Training, Adaptation models, Head, Sensitivity, Computational modeling, Predictive models, visual reasoning BibRef

Petit, G.[Grégoire], Popescu, A.[Adrian], Schindler, H.[Hugo], Picard, D.[David], Delezoide, B.[Bertrand],
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning,
WACV23(3900-3909)
IEEE DOI 2302
Performance evaluation, Location awareness, Codes, Filtering, Feature extraction, Generators, Stability analysis, Vision + language and/or other modalities BibRef

Chandra, D.S.[Dupati Srikar], Varshney, S.[Sakshi], Srijith, P.K., Gupta, S.I.[Sun-Il],
Continual Learning with Dependency Preserving Hypernetworks,
WACV23(2338-2347)
IEEE DOI 2302
Recurrent neural networks, Computational modeling, Task analysis, Image classification, visual reasoning) BibRef

Saha, G.[Gobinda], Roy, K.[Kaushik],
Saliency Guided Experience Packing for Replay in Continual Learning,
WACV23(5262-5272)
IEEE DOI 2302
Location awareness, Learning systems, Visualization, Statistical analysis, Reinforcement learning, visual reasoning) BibRef

Lee, K.Y.[Kuan-Ying], Zhong, Y.[Yuanyi], Wang, Y.X.[Yu-Xiong],
Do Pre-trained Models Benefit Equally in Continual Learning?,
WACV23(6474-6482)
IEEE DOI 2302
Training, Systematics, Codes, Computational modeling, Pipelines, Benchmark testing, Algorithms: Machine learning architectures, and algorithms (including transfer) BibRef

Jiang, J.[Jian], Celiktutan, O.[Oya],
Neural Weight Search for Scalable Task Incremental Learning,
WACV23(1390-1399)
IEEE DOI 2302
Deep learning, Costs, Benchmark testing, Aerospace electronics, Inference algorithms, Task analysis BibRef

Feillet, E.[Eva], Petit, G.[Grégoire], Popescu, A.[Adrian], Reyboz, M.[Marina], Hudelot, C.[Céline],
AdvisIL - A Class-Incremental Learning Advisor,
WACV23(2399-2408)
IEEE DOI 2302
Learning systems, Adaptation models, Codes, Memory management, Training data, Algorithms: Machine learning architectures, visual reasoning) BibRef

Pan, Z.C.[Zi-Cheng], Yu, X.H.[Xiao-Han], Zhang, M.[Miaohua], Gao, Y.S.[Yong-Sheng],
SSFE-Net: Self-Supervised Feature Enhancement for Ultra-Fine-Grained Few-Shot Class Incremental Learning,
WACV23(6264-6273)
IEEE DOI 2302
Knowledge engineering, Visualization, Layout, Self-supervised learning, Benchmark testing, Agriculture BibRef

Krishnan, R., Balaprakash, P.[Prasanna],
Continual Learning via Dynamic Programming,
ICPR22(1350-1356)
IEEE DOI 2212
Partial differential equations, Benchmark testing, Mathematical models, Dynamic programming, Bellman principle BibRef

Bellitto, G.[Giovanni], Pennisi, M.[Matteo], Palazzo, S.[Simone], Bonicelli, L.[Lorenzo], Boschini, M.[Matteo], Calderara, S.[Simone],
Effects of Auxiliary Knowledge on Continual Learning,
ICPR22(1357-1363)
IEEE DOI 2212
Training, Knowledge engineering, Neural networks, Streaming media, Feature extraction, Data models, Task analysis BibRef

Domoguen, J.K.L.[Jansen Keith L.], Naval, P.C.[Prospero C.],
Dynamic Model-Agnostic Meta-Learning for Incremental Few-Shot Learning,
ICPR22(4927-4933)
IEEE DOI 2212
Deep learning, Adaptation models, Particle separators, Prototypes, Benchmark testing, Task analysis BibRef

Hossain, M.S.[Md Sazzad], Saha, P.[Pritom], Chowdhury, T.F.[Townim Faisal], Rahman, S.[Shafin], Rahman, F.[Fuad], Mohammed, N.[Nabeel],
Rethinking Task-Incremental Learning Baselines,
ICPR22(2771-2777)
IEEE DOI 2212
Point cloud compression, Knowledge engineering, Solid modeling, Image recognition, Memory management BibRef

Li, Y.[Yinan], Luo, R.H.[Rong-Hua], Huang, Z.M.[Zhen-Ming],
Class Incremental Learning based on Local Structure Constraints in Feature Space,
ICPR22(2056-2062)
IEEE DOI 2212
Deep learning, Learning systems, Degradation, Training data, Stability analysis, Robustness, Classification algorithms BibRef

Buchert, F.[Felix], Navab, N.[Nassir], Kim, S.T.[Seong Tae],
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning,
ICPR22(2063-2069)
IEEE DOI 2212
Training, Representation learning, Deep learning, Neural networks, Self-supervised learning, Semisupervised learning BibRef

Flesca, S.[Sergio], Mandaglio, D.[Domenico], Scala, F.[Francesco], Tagarelli, A.[Andrea],
Learning to Active Learn by Gradient Variation based on Instance Importance,
ICPR22(2224-2230)
IEEE DOI 2212
Deep learning, Annotations, Source coding, Current measurement, Neural networks, Predictive models BibRef

Wang, R.[Runqi], Bao, Y.X.[Yu-Xiang], Zhang, B.C.[Bao-Chang], Liu, J.Z.[Jian-Zhuang], Zhu, W.T.[Wen-Tao], Guo, G.D.[Guo-Dong],
Anti-retroactive Interference for Lifelong Learning,
ECCV22(XXIV:163-178).
Springer DOI 2211
BibRef

Chen, Z.Z.[Zhuang-Zhuang], Zhang, J.[Jin], Wang, P.[Pan], Chen, J.[Jie], Li, J.Q.[Jian-Qiang],
When Active Learning Meets Implicit Semantic Data Augmentation,
ECCV22(XXV:56-72).
Springer DOI 2211
BibRef

Yi, J.S.K.[John Seon Keun], Seo, M.[Minseok], Park, J.[Jongchan], Choi, D.G.[Dong-Geol],
PT4AL: Using Self-supervised Pretext Tasks for Active Learning,
ECCV22(XXVI:596-612).
Springer DOI 2211
BibRef

Sun, Y.Q.[Yong-Qing], Qin, A.[Anyong], Bandoh, Y.[Yukihiro], Gao, C.Q.[Chen-Qiang], Hiwasaki, Y.[Yusuke],
Active Learning for Hyperspectral Image Classification via Hypergraph Neural Network,
ICIP22(2576-2580)
IEEE DOI 2211
Training, Convolution, Neural networks, Labeling, Faces, Hyperspectral imaging, Hyperspectral Image Classification, Graph Convolution Network BibRef

Hyder, R.[Rakib], Shao, K.[Ken], Hou, B.[Boyu], Markopoulos, P.[Panos], Prater-Bennette, A.[Ashley], Asif, M.S.[M. Salman],
Incremental Task Learning with Incremental Rank Updates,
ECCV22(XXIII:566-582).
Springer DOI 2211
BibRef

Rios, A.[Amanda], Ahuja, N.[Nilesh], Ndiour, I.[Ibrahima], Genc, U.[Utku], Itti, L.[Laurent], Tickoo, O.[Omesh],
incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection,
ECCV22(XXV:588-604).
Springer DOI 2211
BibRef

He, J.P.[Jiang-Peng], Zhu, F.Q.[Feng-Qing],
Exemplar-Free Online Continual Learning,
ICIP22(541-545)
IEEE DOI 2211
Training, Privacy, Protocols, Benchmark testing, Task analysis, Tuning, Continual learning, Online scenario, Exemplar-free, Image classification BibRef

Michel, N.[Nicolas], Negrel, R.[Romain], Chierchia, G.[Giovanni], Bercher, J.F.[Jean-Fmnçois],
Contrastive Learning for Online Semi-Supervised General Continual Learning,
ICIP22(1896-1900)
IEEE DOI 2211
Training, Memory management, Continual Learning, Contrastive Learning, Semi-Supervised Learning, Memory BibRef

Ye, F.[Fei], Bors, A.G.[Adrian G.],
Learning an Evolved Mixture Model for Task-Free Continual Learning,
ICIP22(1936-1940)
IEEE DOI 2211
Training, Deep learning, Adaptation models, Mixture models, Network architecture, Benchmark testing, Hilbert Schmidt Independence Criterion BibRef

Guimeng, L.[Liu], Yang, G.[Guo], Yin, C.W.S.[Cheryl Wong Sze], Suganathan, P.N.[Ponnuthurai Nagartnam], Savitha, R.[Ramasamy],
Unsupervised Generative Variational Continual Learning,
ICIP22(4028-4032)
IEEE DOI 2211
Training, Adaptation models, Uncertainty, Image coding, Neurons, Benchmark testing, Task analysis, Continual Learning, Unsupervised, Variational Inference BibRef

Ashok, A.[Arjun], Joseph, K.J., Balasubramanian, V.N.[Vineeth N.],
Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer,
ECCV22(XXVII:105-122).
Springer DOI 2211
BibRef

Kalla, J.[Jayateja], Biswas, S.[Soma],
S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning,
ECCV22(XXV:432-448).
Springer DOI 2211
BibRef

Peng, C.[Can], Zhao, K.[Kun], Wang, T.R.[Tian-Ren], Li, M.[Meng], Lovell, B.C.[Brian C.],
Few-Shot Class-Incremental Learning from an Open-Set Perspective,
ECCV22(XXV:382-397).
Springer DOI 2211
BibRef

Wang, F.Y.[Fu-Yun], Zhou, D.W.[Da-Wei], Ye, H.J.[Han-Jia], Zhan, D.C.[De-Chuan],
FOSTER: Feature Boosting and Compression for Class-Incremental Learning,
ECCV22(XXV:398-414).
Springer DOI 2211
BibRef

Gao, Q.[Qiankun], Zhao, C.[Chen], Ghanem, B.[Bernard], Zhang, J.[Jian],
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning,
ECCV22(XXIII:423-439).
Springer DOI 2211
BibRef

Findik, Y.[Yasin], Pourkamali-Anaraki, F.[Farhad],
D-CBRS: Accounting for Intra-Class Diversity in Continual Learning,
ICIP22(2531-2535)
IEEE DOI 2211
Memory management, Reservoirs, Data models, Continual learning, lifelong learning, catastrophic forgetting, class-incremental learning BibRef

Liu, X.L.[Xia-Lei], Hu, Y.S.[Yu-Song], Cao, X.S.[Xu-Sheng], Bagdanov, A.D.[Andrew D.], Li, K.[Ke], Cheng, M.M.[Ming-Ming],
Long-Tailed Class Incremental Learning,
ECCV22(XXXIII:495-512).
Springer DOI 2211
BibRef

Kothawade, S.[Suraj], Ghosh, S.[Saikat], Shekhar, S.[Sumit], Xiang, Y.[Yu], Iyer, R.[Rishabh],
Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information,
ECCV22(XXXVIII:1-16).
Springer DOI 2211
BibRef

Pourcel, J.[Julien], Vu, N.S.[Ngoc-Son], French, R.M.[Robert M.],
Online Task-free Continual Learning with Dynamic Sparse Distributed Memory,
ECCV22(XXV:739-756).
Springer DOI 2211
BibRef

Kong, Y.[Yajing], Liu, L.[Liu], Wang, Z.[Zhen], Tao, D.C.[Da-Cheng],
Balancing Stability and Plasticity Through Advanced Null Space in Continual Learning,
ECCV22(XXVI:219-236).
Springer DOI 2211
BibRef

Wang, L.Y.[Li-Yuan], Zhang, X.X.[Xing-Xing], Li, Q.[Qian], Zhu, J.[Jun], Zhong, Y.[Yi],
CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One,
ECCV22(XXVI:254-271).
Springer DOI 2211
BibRef

Purushwalkam, S.[Senthil], Morgado, P.[Pedro], Gupta, A.[Abhinav],
The Challenges of Continuous Self-Supervised Learning,
ECCV22(XXVI:702-721).
Springer DOI 2211
BibRef

Shon, H.[Hyounguk], Lee, J.[Janghyeon], Kim, S.H.[Seung Hwan], Kim, J.[Junmo],
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning,
ECCV22(XXXIII:513-529).
Springer DOI 2211
BibRef

Wang, Z.F.[Zi-Feng], Zhang, Z.Z.[Zi-Zhao], Ebrahimi, S.[Sayna], Sun, R.[Ruoxi], Zhang, H.[Han], Lee, C.Y.[Chen-Yu], Ren, X.Q.[Xiao-Qi], Su, G.L.[Guo-Long], Perot, V.[Vincent], Dy, J.[Jennifer], Pfister, T.[Tomas],
DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning,
ECCV22(XXVI:631-648).
Springer DOI 2211
BibRef

Hedegaard, L.[Lukas], Iosifidis, A.[Alexandros],
Continual 3D Convolutional Neural Networks for Real-time Processing of Videos,
ECCV22(IV:369-385).
Springer DOI 2211
BibRef

Jin, H.[Hyundong], Kim, E.[Eunwoo],
Helpful or Harmful: Inter-task Association in Continual Learning,
ECCV22(XI:519-535).
Springer DOI 2211
BibRef

Andle, J.[Joshua], Sekeh, S.Y.[Salimeh Yasaei],
Theoretical Understanding of the Information Flow on Continual Learning Performance,
ECCV22(XII:86-101).
Springer DOI 2211
BibRef

Campari, T.[Tommaso], Lamanna, L.[Leonardo], Traverso, P.[Paolo], Serafini, L.[Luciano], Ballan, L.[Lamberto],
Online Learning of Reusable Abstract Models for Object Goal Navigation,
CVPR22(14850-14859)
IEEE DOI 2210
Image segmentation, Navigation, Computational modeling, Machine vision, Robot vision systems, Benchmark testing, Vision applications and systems BibRef

Araujo, V.[Vladimir], Hurtado, J.[Julio], Soto, A.[Alvaro], Moens, M.F.[Marie-Francine],
Entropy-based Stability-Plasticity for Lifelong Learning,
CLVision22(3720-3727)
IEEE DOI 2210
Training, Deep learning, Computational modeling, Natural languages, Neural networks, Interference, Transformers BibRef

Wang, C.[Chen], Qiu, Y.H.[Yu-Heng], Gao, D.[Dasong], Scherer, S.[Sebastian],
Lifelong Graph Learning,
CVPR22(13709-13718)
IEEE DOI 2210
Bridges, Training, Performance evaluation, Network topology, Wearable computers, Graph neural networks, Topology, Statistical methods BibRef

Yu, W.P.[Wei-Ping], Zhu, S.[Sijie], Yang, T.[Taojiannan], Chen, C.[Chen],
Consistency-based Active Learning for Object Detection,
L3D-IVU22(3950-3959)
IEEE DOI 2210
Learning systems, Measurement, Object detection, Detectors, Pattern recognition BibRef

Parvaneh, A.[Amin], Abbasnejad, E.[Ehsan], Teney, D.[Damien], Haffari, R.[Reza], van den Hengel, A.J.[Anton J.], Shi, J.Q.F.[Javen Qin-Feng],
Active Learning by Feature Mixing,
CVPR22(12227-12236)
IEEE DOI 2210
Interpolation, Costs, Codes, Machine vision, Predictive models, Transformers, Efficient learning and inferences, Vision applications and systems BibRef

Munjal, P.[Prateek], Hayat, N.[Nasir], Hayat, M.[Munawar], Sourati, J.[Jamshid], Khan, S.[Shadab],
Towards Robust and Reproducible Active Learning using Neural Networks,
CVPR22(223-232)
IEEE DOI 2210
Measurement, Uncertainty, Costs, Codes, Annotations, Neural networks, Machine learning, Efficient learning and inferences, privacy and ethics in vision BibRef

Wu, J.X.[Jia-Xi], Chen, J.X.[Jia-Xin], Huang, D.[Di],
Entropy-based Active Learning for Object Detection with Progressive Diversity Constraint,
CVPR22(9387-9396)
IEEE DOI 2210
Learning systems, Uncertainty, Costs, Prototypes, Object detection, Entropy, Recognition: detection, categorization, retrieval BibRef

Lin, G.L.[Guo-Liang], Chu, H.[Hanlu], Lai, H.J.[Han-Jiang],
Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector,
CVPR22(89-98)
IEEE DOI 2210
Connectors, Knowledge engineering, Upper bound, Codes, Neural networks, Training data, Machine learning, Transfer/low-shot/long-tail learning BibRef

Bhunia, A.K.[Ayan Kumar], Gajjala, V.R.[Viswanatha Reddy], Koley, S.[Subhadeep], Kundu, R.[Rohit], Sain, A.[Aneeshan], Xiang, T.[Tao], Song, Y.Z.[Yi-Zhe],
Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches,
CVPR22(2283-2292)
IEEE DOI 2210
Knowledge engineering, Ethics, Visualization, Technological innovation, Privacy, Data privacy, Message passing, Vision applications and systems BibRef

Joseph, K.J., Khan, S.[Salman], Khan, F.S.[Fahad Shahbaz], Anwer, R.M.[Rao Muhammad], Balasubramanian, V.N.[Vineeth N],
Energy-based Latent Aligner for Incremental Learning,
CVPR22(7442-7451)
IEEE DOI 2210
Manifolds, Deep learning, Codes, Pipelines, Object detection, Detectors, Recognition: detection, categorization, retrieval, Transfer/low-shot/long-tail learning BibRef

Cermelli, F.[Fabio], Geraci, A.[Antonino], Fontanel, D.[Dario], Caputo, B.[Barbara],
Modeling Missing Annotations for Incremental Learning in Object Detection,
CLVision22(3699-3709)
IEEE DOI 2210
Training, Annotations, Training data, Object detection, Detectors, Predictive models BibRef

Tiwari, R.[Rishabh], Killamsetty, K.[Krishnateja], Iyer, R.[Rishabh], Shenoy, P.[Pradeep],
GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning,
CVPR22(99-108)
IEEE DOI 2210
Adaptation models, Data models, Pattern recognition, Task analysis, Optimization, Machine learning, Computer vision theory, Optimization methods BibRef

Yan, Q.S.[Qing-Sen], Gong, D.[Dong], Liu, Y.H.[Yu-Hang], van den Hengel, A.[Anton], Shi, J.Q.F.[Javen Qin-Feng],
Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning,
CVPR22(109-118)
IEEE DOI 2210
Correlation, Neurons, Interference, Machine learning, Reservoirs, Bayes methods, Machine learning, Deep learning architectures and techniques BibRef

Wang, Z.[Zifeng], Zhang, Z.[Zizhao], Lee, C.Y.[Chen-Yu], Zhang, H.[Han], Sun, R.[Ruoxi], Ren, X.Q.[Xiao-Qi], Su, G.[Guolong], Perot, V.[Vincent], Dy, J.[Jennifer], Pfister, T.[Tomas],
Learning to Prompt for Continual Learning,
CVPR22(139-149)
IEEE DOI 2210
Representation learning, Adaptation models, Codes, Predictive models, Data models, Pattern recognition, Representation learning BibRef

Xue, M.Q.[Meng-Qi], Zhang, H.[Haofei], Song, J.[Jie], Song, M.L.[Ming-Li],
Meta-attention for ViT-backed Continual Learning,
CVPR22(150-159)
IEEE DOI 2210
Learning systems, Degradation, Codes, Transformers, Pattern recognition, Convolutional neural networks, Deep learning architectures and techniques BibRef

Wang, Z.[Zhen], Liu, L.[Liu], Kong, Y.J.[Ya-Jing], Guo, J.X.[Jia-Xian], Tao, D.C.[Da-Cheng],
Online Continual Learning with Contrastive Vision Transformer,
ECCV22(XX:631-650).
Springer DOI 2211
BibRef

Wang, Z.[Zhen], Liu, L.[Liu], Duan, Y.Q.[Yi-Qun], Kong, Y.J.[Ya-Jing], Tao, D.C.[Da-Cheng],
Continual Learning with Lifelong Vision Transformer,
CVPR22(171-181)
IEEE DOI 2210
Training, Learning systems, Neural networks, Interference, Benchmark testing, Transformers, Machine learning, Others, Representation learning BibRef

Gu, Y.[Yanan], Yang, X.[Xu], Wei, K.[Kun], Deng, C.[Cheng],
Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency,
CVPR22(7432-7441)
IEEE DOI 2210
Training, Representation learning, Semantics, Neural networks, Benchmark testing, Streaming media, Recognition: detection, Representation learning BibRef

Simon, C.[Christian], Faraki, M.[Masoud], Tsai, Y.H.[Yi-Hsuan], Yu, X.[Xiang], Schulter, S.[Samuel], Suh, Y.[Yumin], Harandi, M.[Mehrtash], Chandraker, M.[Manmohan],
On Generalizing Beyond Domains in Cross-Domain Continual Learning,
CVPR22(9255-9264)
IEEE DOI 2210
Knowledge engineering, Measurement, Deep learning, Computational modeling, Neural networks, Pattern recognition, Machine learning BibRef

Bang, J.[Jihwan], Koh, H.[Hyunseo], Park, S.[Seulki], Song, H.[Hwanjun], Ha, J.W.[Jung-Woo], Choi, J.H.[Jong-Hyun],
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries,
CVPR22(9265-9274)
IEEE DOI 2210
Art, Codes, Memory management, Semisupervised learning, Pattern recognition, Noise measurement, Self- semi- meta- unsupervised learning BibRef

Douillard, A.[Arthur], Ramé, A.[Alexandre], Couairon, G.[Guillaume], Cord, M.[Matthieu],
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion,
CVPR22(9275-9285)
IEEE DOI 2210
Representation learning, Deep learning, Memory management, Network architecture, Transformers, Market research, Decoding, Representation learning BibRef

Zhu, K.[Kai], Zhai, W.[Wei], Cao, Y.[Yang], Luo, J.B.[Jie-Bo], Zha, Z.J.[Zheng-Jun],
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning,
CVPR22(9286-9295)
IEEE DOI 2210
Fuses, Prototypes, Benchmark testing, Pattern recognition, Task analysis, Optimization, Representation learning BibRef

Fini, E.[Enrico], da Costa, V.G.T.[Victor G. Turrisi], Alameda-Pineda, X.[Xavier], Ricci, E.[Elisa], Alahari, K.[Karteek], Mairal, J.[Julien],
Self-Supervised Models are Continual Learners,
CVPR22(9611-9620)
IEEE DOI 2210
Training, Representation learning, Visualization, Surveillance, Self-supervised learning, Data models, Self- semi- meta- Representation learning BibRef

Chen, G.[Geng], Zhang, W.D.[Wen-Dong], Lu, H.[Han], Gao, S.[Siyu], Wang, Y.[Yunbo], Long, M.S.[Ming-Sheng], Yang, X.K.[Xiao-Kang],
Continual Predictive Learning from Videos,
CVPR22(10718-10727)
IEEE DOI 2210
Training, Adaptation models, Art, Predictive models, Benchmark testing, Prediction algorithms, Self- semi- meta- unsupervised learning BibRef

Wan, T.S.T.[Timmy S. T.], Chen, J.C.[Jun-Cheng], Wu, T.Y.[Tzer-Yi], Chen, C.S.[Chu-Song],
Continual Learning for Visual Search with Backward Consistent Feature Embedding,
CVPR22(16681-16690)
IEEE DOI 2210
Representation learning, Visualization, Computational modeling, Coherence, Benchmark testing, Data models, Representation learning, retrieval BibRef

Davari, M.R.[Mohammad-Reza], Asadi, N.[Nader], Mudur, S.[Sudhir], Aljundi, R.[Rahaf], Belilovsky, E.[Eugene],
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning,
CVPR22(16691-16700)
IEEE DOI 2210
Representation learning, Training, Measurement, Supervised learning, Neural networks, Prototypes, Representation learning BibRef

Shi, Y.J.[Yu-Jun], Zhou, K.Q.[Kuang-Qi], Liang, J.[Jian], Jiang, Z.[Zihang], Feng, J.S.[Jia-Shi], Torr, P.H.S.[Philip H.S.], Bai, S.[Song], Tan, V.Y.F.[Vincent Y.F.],
Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning,
CVPR22(16701-16710)
IEEE DOI 2210
Training, Representation learning, Codes, Computational modeling, Benchmark testing, Pattern recognition, Representation learning, retrieval BibRef

Taufique, A.M.N.[Abu Md Niamul], Jahan, C.S.[Chowdhury Sadman], Savakis, A.[Andreas],
Unsupervised Continual Learning for Gradually Varying Domains,
CLVision22(3739-3749)
IEEE DOI 2210
Learning systems, Bridges, Adaptation models, Codes, Memory management BibRef

Ermis, B.[Beyza], Zappella, G.[Giovanni], Wistuba, M.[Martin], Rawal, A.[Aditya], Archambeau, C.[Cédric],
Continual Learning with Transformers for Image Classification,
CLVision22(3773-3780)
IEEE DOI 2210
Training, Adaptation models, Computational modeling, Neural networks, Training data, Predictive models BibRef

Carta, A.[Antonio], Cossu, A.[Andrea], Lomonaco, V.[Vincenzo], Bacciu, D.[Davide],
Ex-Model: Continual Learning from a Stream of Trained Models,
CLVision22(3789-3798)
IEEE DOI 2210
Learning systems, Data privacy, Computational modeling, Data models, Pattern recognition BibRef

Jie, S.[Shibo], Deng, Z.H.[Zhi-Hong], Li, Z.H.[Zi-Heng],
Alleviating Representational Shift for Continual Fine-tuning,
CLVision22(3809-3818)
IEEE DOI 2210
Training, Pattern recognition, Task analysis, Testing BibRef

Pelosin, F.[Francesco], Jha, S.[Saurav], Torsello, A.[Andrea], Raducanu, B.[Bogdan], van de Weijer, J.[Joost],
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization,
CLVision22(3819-3828)
IEEE DOI 2210
Learning systems, Weight measurement, Image recognition, Surgery, Benchmark testing, Transformers, Stability analysis BibRef

He, J.P.[Jiang-Peng], Zhu, F.Q.[Feng-Qing],
Out-Of-Distribution Detection In Unsupervised Continual Learning,
CLVision22(3849-3854)
IEEE DOI 2210
Protocols, Annotations, Detectors, Pattern recognition, Task analysis BibRef

Kim, G.[Gyuhak], Esmaeilpour, S.[Sepideh], Xiao, C.[Changnan], Liu, B.[Bing],
Continual Learning Based on OOD Detection and Task Masking,
CLVision22(3855-3865)
IEEE DOI 2210
Training, Machine learning algorithms, Codes, Supervised learning, Data models BibRef

Gomez-Villa, A.[Alex], Twardowski, B.[Bartlomiej], Yu, L.[Lu], Bagdanov, A.D.[Andrew D.], van de Weijer, J.[Joost],
Continually Learning Self-Supervised Representations with Projected Functional Regularization,
CLVision22(3866-3876)
IEEE DOI 2210
Conferences, Self-supervised learning, Image representation, Pattern recognition BibRef

Karim, N.[Nazmul], Khalid, U.[Umar], Esmaeili, A.[Ashkan], Rahnavard, N.[Nazanin],
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning,
CLVision22(3877-3887)
IEEE DOI 2210
Training, Codes, Purification, Benchmark testing, Performance gain BibRef

Merlin, G.[Gabriele], Lomonaco, V.[Vincenzo], Cossu, A.[Andrea], Carta, A.[Antonio], Bacciu, D.[Davide],
Practical Recommendations for Replay-Based Continual Learning Methods,
CL4REAL22(548-559).
Springer DOI 2208
BibRef

Kim, S.[Sohee], Lee, S.K.[Seung-Kyu],
Continual Learning with Neuron Activation Importance,
CIAP22(I:310-321).
Springer DOI 2205
BibRef

Lucchesi, N.[Nicoló], Carta, A.[Antonio], Lomonaco, V.[Vincenzo], Bacciu, D.[Davide],
Avalanche RL: A Continual Reinforcement Learning Library,
CIAP22(I:524-535).
Springer DOI 2205
BibRef

Barletti, T.[Tommaso], Biondi, N.[Niccoló], Pernici, F.[Federico], Bruni, M.[Matteo], del Bimbo, A.[Alberto],
Contrastive Supervised Distillation for Continual Representation Learning,
CIAP22(I:597-609).
Springer DOI 2205
BibRef

Davalas, C.[Charalampos], Michail, D.[Dimitrios], Diou, C.[Christos], Varlamis, I.[Iraklis], Tserpes, K.[Konstantinos],
Computationally Efficient Rehearsal for Online Continual Learning,
CIAP22(III:39-49).
Springer DOI 2205
BibRef

Zhong, Y.J.[Yi-Jie], Sun, Z.X.[Zheng-Xing], Luo, S.T.[Shou-Tong], Sun, Y.H.[Yun-Han], Zhang, W.[Wei],
Category-Sensitive Incremental Learning for Image-Based 3D Shape Reconstruction,
MMMod22(I:231-244).
Springer DOI 2203
BibRef

Guo, J.N.[Jian-Nan], hi, H.C.S.[Hao-Chen S], Kang, Y.Y.[Yang-Yang], Kuang, K.[Kun], Tang, S.L.[Si-Liang], Jiang, Z.R.[Zhuo-Ren], Sun, C.L.[Chang-Long], Wu, F.[Fei], Zhuang, Y.T.[Yue-Ting],
Semi-supervised Active Learning for Semi-supervised Models: Exploit Adversarial Examples with Graph-based Virtual Labels,
ICCV21(2876-2885)
IEEE DOI 2203
Costs, Computational modeling, Clustering algorithms, Semisupervised learning, Rendering (computer graphics), Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Huang, S.[Siyu], Wang, T.Y.[Tian-Yang], Xiong, H.Y.[Hao-Yi], Huan, J.[Jun], Dou, D.[Dejing],
Semi-Supervised Active Learning with Temporal Output Discrepancy,
ICCV21(3427-3436)
IEEE DOI 2203
Training, Image segmentation, Annotations, Semantics, Loss measurement, Data models, Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Liu, Z.M.[Zhuo-Ming], Ding, H.[Hao], Zhong, H.P.[Hua-Ping], Li, W.J.[Wei-Jia], Dai, J.F.[Ji-Feng], He, C.H.[Cong-Hui],
Influence Selection for Active Learning,
ICCV21(9254-9263)
IEEE DOI 2203
Learning systems, Costs, Uncertainty, Annotations, Computational modeling, Neural networks, Recognition and classification BibRef

Choi, J.[Jiwoong], Elezi, I.[Ismail], Lee, H.J.[Hyuk-Jae], Farabet, C.[Clement], Alvarez, J.M.[Jose M.],
Active Learning for Deep Object Detection via Probabilistic Modeling,
ICCV21(10244-10253)
IEEE DOI 2203
Location awareness, Uncertainty, Costs, Head, Computational modeling, Object detection, Performance gain, Representation learning, Detection and localization in 2D and 3D BibRef

Smith, J.[James], Hsu, Y.C.[Yen-Chang], Balloch, J.[Jonathan], Shen, Y.[Yilin], Jin, H.X.[Hong-Xia], Kira, Z.[Zsolt],
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning,
ICCV21(9354-9364)
IEEE DOI 2203
Training, Learning systems, Law, Memory management, Training data, Benchmark testing, Recognition and classification BibRef

Wu, G.[Guile], Gong, S.G.[Shao-Gang], Queen, P.L.[Pan Li],
Striking a Balance between Stability and Plasticity for Class-Incremental Learning,
ICCV21(1104-1113)
IEEE DOI 2203
Bridges, Computational modeling, Benchmark testing, Stability analysis, Recognition and classification, Optimization and learning methods BibRef

Ahn, H.[Hongjoon], Kwak, J.[Jihwan], Lim, S.B.[Su-Bin], Bang, H.[Hyeonsu], Kim, H.[Hyojun], Moon, T.[Taesup],
SS-IL: Separated Softmax for Incremental Learning,
ICCV21(824-833)
IEEE DOI 2203
Systematics, Training data, Benchmark testing, Task analysis, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Yan, Z.[Zike], Tian, Y.X.[Yu-Xin], Shi, X.S.[Xue-Song], Guo, P.[Ping], Wang, P.[Peng], Zha, H.B.[Hong-Bin],
Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations,
ICCV21(15762-15772)
IEEE DOI 2203
Geometry, Robot kinematics, Neural networks, Streaming media, Network architecture, Real-time systems, Scene analysis and understanding BibRef

Kim, C.D.[Chris Dongjoo], Jeong, J.[Jinseo], Moon, S.[Sangwoo], Kim, G.[Gunhee],
Continual Learning on Noisy Data Streams via Self-Purified Replay,
ICCV21(517-527)
IEEE DOI 2203
Training, Heart, Buildings, Information filters, Noise measurement, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

de Lange, M.[Matthias], Tuytelaars, T.[Tinne],
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams,
ICCV21(8230-8239)
IEEE DOI 2203
Training, Representation learning, Memory management, Prototypes, Benchmark testing, Linear programming, Synchronization, Representation learning BibRef

Verwimp, E.[Eli], de Lange, M.[Matthias], Tuytelaars, T.[Tinne],
Rehearsal revealed: The limits and merits of revisiting samples in continual learning,
ICCV21(9365-9374)
IEEE DOI 2203
Computational modeling, Machine learning, Benchmark testing, Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification BibRef

Cha, H.[Hyuntak], Lee, J.[Jaeho], Shin, J.[Jinwoo],
Co2L: Contrastive Continual Learning,
ICCV21(9496-9505)
IEEE DOI 2203
Representation learning, Visualization, Codes, Computational modeling, Benchmark testing, Representation learning BibRef

Cai, Z.P.[Zhi-Peng], Sener, O.[Ozan], Koltun, V.[Vladlen],
Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data,
ICCV21(8261-8270)
IEEE DOI 2203
Training, Measurement, Visualization, Schedules, Supervised learning, Coherence, Benchmark testing, Vision + other modalities BibRef

Lee, E.[Eugene], Huang, C.H.[Cheng-Han], Lee, C.Y.[Chen-Yi],
Few-Shot and Continual Learning with Attentive Independent Mechanisms,
ICCV21(9435-9444)
IEEE DOI 2203
Training, Deep learning, Adaptation models, Codes, Art, Computational modeling, Visual reasoning and logical representation BibRef

Kukleva, A.[Anna], Kuehne, H.[Hilde], Schiele, B.[Bernt],
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting,
ICCV21(9000-9009)
IEEE DOI 2203
Training, Deep learning, Benchmark testing, Entropy, Calibration, Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification BibRef

Cheraghian, A.[Ali], Rahman, S.[Shafin], Ramasinghe, S.[Sameera], Fang, P.F.[Peng-Fei], Simon, C.[Christian], Petersson, L.[Lars], Harandi, M.[Mehrtash],
Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces,
ICCV21(8641-8650)
IEEE DOI 2203
Training, Visualization, Adaptation models, Computational modeling, Semantics, Prototypes, BibRef

Bengar, J.Z.[Javad Zolfaghari], van de Weijer, J.[Joost], Fuentes, L.L.[Laura Lopez], Raducanu, B.[Bogdan],
Class-Balanced Active Learning for Image Classification,
WACV22(3707-3716)
IEEE DOI 2202
Learning systems, Performance gain, Classification algorithms, Labeling, Optimization, Learning and Optimization BibRef

Aljundi, R.[Rahaf], Chumerin, N.[Nikolay], Reino, D.O.[Daniel Olmeda],
Identifying Wrongly Predicted Samples: A Method for Active Learning,
WACV22(2071-2079)
IEEE DOI 2202
Learning systems, Uncertainty, Systematics, Limiting, Annotations, Computational modeling, Predictive models, Deep Learning Active Learning BibRef

Gopalakrishnan, S.[Saisubramaniam], Singh, P.R.[Pranshu Ranjan], Fayek, H.[Haytham], Ramasamy, S.[Savitha], Ambikapathi, A.M.[Arul-Murugan],
Knowledge Capture and Replay for Continual Learning,
WACV22(337-345)
IEEE DOI 2202
Training, Deep learning, Visualization, Data privacy, Noise reduction, Neural networks, Knowledge representation, Semi- and Un- supervised Learning Continual Learning BibRef

He, J.P.[Jiang-Peng], Zhu, F.Q.[Feng-Qing],
Online Continual Learning Via Candidates Voting,
WACV22(1292-1301)
IEEE DOI 2202
Training, Data privacy, Memory management, Benchmark testing, Task analysis, Image classification, Vision Systems and Applications BibRef

Zhang, H.[Heng], Fromont, E.[Elisa], Lefevre, S.[Sébastien], Avignon, B.[Bruno],
Deep Active Learning from Multispectral Data Through Cross-Modality Prediction Inconsistency,
ICIP21(449-453)
IEEE DOI 2201
Image segmentation, Image analysis, Redundancy, Manuals, Sensor fusion, Robustness, Sensors, Active learning, multiple sensor fusion BibRef

Pham, X.C.[Xuan Cuong], Liew, A.W.C.[Alan Wee-Chung], Wang, C.[Can],
A Novel Class-wise Forgetting Detector in Continual Learning,
DICTA21(01-08)
IEEE DOI 2201
Training, Learning systems, Deep learning, Adaptation models, Digital images, Detectors, Data models, Online learning, Deep learning BibRef

Singh, P.R.[Pranshu Ranjan], Gopalakrishnan, S.[Saisubramaniam], ZhongZheng, Q.[Qiao], Suganthan, P.N.[Ponnuthurai N.], Ramasamy, S.[Savitha], Ambikapathi, A.[ArulMurugan],
Task-Agnostic Continual Learning Using Base-Child Classifiers,
ICIP21(794-798)
IEEE DOI 2201
Image processing, Complexity theory, Classification algorithms, Task analysis, Standards, Continual Learning, Hybrid Networks BibRef

Bengar, J.Z.[Javad Zolfaghari], Raducanu, B.[Bogdan], van de Weijer, J.[Joost],
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning,
CAIP21(I:403-413).
Springer DOI 2112
BibRef

Sreenivasaiah, D.[Deepthi], Otterbach, J.[Johannes], Wollmann, T.[Thomas],
MEAL: Manifold Embedding-based Active Learning,
ERCVAD21(1029-1037)
IEEE DOI 2112
Manifolds, Learning systems, Image segmentation, Uncertainty, Measurement uncertainty, Training data, Entropy BibRef

Bengar, J.Z.[Javad Zolfaghari], van de Weijer, J.[Joost], Twardowski, B.[Bartlomiej], Raducanu, B.[Bogdan],
Reducing Label Effort: Self-Supervised meets Active Learning,
ILDAV21(1631-1639)
IEEE DOI 2112
Training, Annotations, Supervised learning, Labeling, Object recognition BibRef

Oren, G.[Guy], Wolf, L.B.[Lior B.],
In Defense of the Learning Without Forgetting for Task Incremental Learning,
DeepMTL21(2209-2218)
IEEE DOI 2112
Learning systems, Codes, Roads, Solids BibRef

Yan, Z.[Zike], Wang, X.[Xin], Zha, H.B.[Hong-Bin],
Online Learning of a Probabilistic and Adaptive Scene Representation,
CVPR21(13106-13116)
IEEE DOI 2111
Geometry, Adaptation models, Computational modeling, Mixture models, Probability density function, Data models BibRef

Pang, B.[Bo], Peng, G.[Gao], Li, Y.Z.[Yi-Zhuo], Lu, C.[Cewu],
PGT: A Progressive Method for Training Models on Long Videos,
CVPR21(11374-11384)
IEEE DOI 2111
Training, Convolutional codes, Computational modeling, Video sequences, Semantics, Markov processes BibRef

Wang, X.D.[Xu-Dong], Lian, L.[Long], Yu, S.X.[Stella X.],
Unsupervised Visual Attention and Invariance for Reinforcement Learning,
CVPR21(6673-6683)
IEEE DOI 2111
Training, Visualization, Annotations, Reinforcement learning, Manuals, Benchmark testing BibRef

Singh, P.[Pravendra], Mazumder, P.[Pratik], Rai, P.[Piyush], Namboodiri, V.P.[Vinay P.],
Rectification-based Knowledge Retention for Continual Learning,
CVPR21(15277-15286)
IEEE DOI 2111
Learning systems, Training, Deep learning, Adaptation models, Pattern recognition, Task analysis BibRef

Shi, Y.J.[Yu-Jun], Yuan, L.[Li], Chen, Y.P.[Yun-Peng], Feng, J.S.[Jia-Shi],
Continual Learning via Bit-Level Information Preserving,
CVPR21(16669-16678)
IEEE DOI 2111
Quantization (signal), Costs, Neural networks, Memory management, Reinforcement learning, Distance measurement, Pattern recognition BibRef

Verma, V.K.[Vinay Kumar], Liang, K.J.[Kevin J], Mehta, N.[Nikhil], Rai, P.[Piyush], Carin, L.[Lawrence],
Efficient Feature Transformations for Discriminative and Generative Continual Learning,
CVPR21(13860-13870)
IEEE DOI 2111
Learning systems, Computational modeling, Scalability, Neural networks, Transforms, Predictive models BibRef

Tang, S.X.[Shi-Xiang], Chen, D.P.[Da-Peng], Zhu, J.[Jinguo], Yu, S.J.[Shi-Jie], Ouyang, W.L.[Wan-Li],
Layerwise Optimization by Gradient Decomposition for Continual Learning,
CVPR21(9629-9638)
IEEE DOI 2111
Knowledge engineering, Deep learning, Computational modeling, Benchmark testing, Pattern recognition, Task analysis BibRef

Wang, S.P.[Shi-Peng], Li, X.R.[Xiao-Rong], Sun, J.[Jian], Xu, Z.B.[Zong-Ben],
Training Networks in Null Space of Feature Covariance for Continual Learning,
CVPR21(184-193)
IEEE DOI 2111
Training, Null space, Benchmark testing, Approximation algorithms, Stability analysis, Pattern recognition BibRef

Volpi, R.[Riccardo], Larlus, D.[Diane], Rogez, G.[Grégory],
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning,
CVPR21(4441-4451)
IEEE DOI 2111
Visualization, Adaptation models, Image segmentation, Computational modeling, Semantics, Pattern recognition BibRef

Bang, J.[Jihwan], Kim, H.[Heesu], Yoo, Y.J.[Young-Joon], Ha, J.W.[Jung-Woo], Choi, J.H.[Jong-Hyun],
Rainbow Memory: Continual Learning with a Memory of Diverse Samples,
CVPR21(8214-8223)
IEEE DOI 2111
Training, Uncertainty, Codes, Memory management, Learning (artificial intelligence), Sampling methods BibRef

Simon, C.[Christian], Koniusz, P.[Piotr], Harandi, M.[Mehrtash],
On Learning the Geodesic Path for Incremental Learning,
CVPR21(1591-1600)
IEEE DOI 2111
Manifolds, Knowledge engineering, Neural networks, Linear programming, Pattern recognition, Task analysis BibRef

Wu, Z.Y.[Zi-Yang], Baek, C.[Christina], You, C.[Chong], Ma, Y.[Yi],
Incremental Learning via Rate Reduction,
CVPR21(1125-1133)
IEEE DOI 2111
Deep learning, Training, Backpropagation, Computational modeling, Data models BibRef

Liu, Y.Y.[Yao-Yao], Schiele, B.[Bernt], Sun, Q.[Qianru],
Adaptive Aggregation Networks for Class-Incremental Learning,
CVPR21(2544-2553)
IEEE DOI 2111
Adaptation models, Adaptive systems, Network architecture, Benchmark testing, Stability analysis BibRef

Yan, S.P.[Shi-Peng], Xie, J.W.[Jiang-Wei], He, X.M.[Xu-Ming],
DER: Dynamically Expandable Representation for Class Incremental Learning,
CVPR21(3013-3022)
IEEE DOI 2111
Visualization, Adaptation models, Benchmark testing, Feature extraction, Pattern recognition, Complexity theory BibRef

Hu, X.[Xinting], Tang, K.[Kaihua], Miao, C.Y.[Chun-Yan], Hua, X.S.[Xian-Sheng], Zhang, H.[Hanwang],
Distilling Causal Effect of Data in Class-Incremental Learning,
CVPR21(3956-3965)
IEEE DOI 2111
Training, Costs, Streaming media, Benchmark testing, Pattern recognition, Reliability BibRef

Wang, L.Y.[Li-Yuan], Yang, K.[Kuo], Li, C.X.[Chong-Xuan], Hong, L.Q.[Lan-Qing], Li, Z.G.[Zhen-Guo], Zhu, J.[Jun],
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning,
CVPR21(5379-5388)
IEEE DOI 2111
Deep learning, Systematics, Semisupervised learning, Benchmark testing, Generators BibRef

Zhu, K.[Kai], Cao, Y.[Yang], Zhai, W.[Wei], Cheng, J.[Jie], Zha, Z.J.[Zheng-Jun],
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning,
CVPR21(6797-6806)
IEEE DOI 2111
Adaptation models, Computational modeling, Prototypes, Benchmark testing, Power capacitors, Pattern recognition BibRef

Abdelsalam, M.[Mohamed], Faramarzi, M.[Mojtaba], Sodhani, S.[Shagun], Chandar, S.[Sarath],
IIRC: Incremental Implicitly-Refined Classification,
CVPR21(11033-11042)
IEEE DOI 2111
Analytical models, Computational modeling, Benchmark testing, Prediction algorithms, Pattern recognition, Classification algorithms BibRef

Zhang, C.[Chi], Song, N.[Nan], Lin, G.S.[Guo-Sheng], Zheng, Y.[Yun], Pan, P.[Pan], Xu, Y.H.[Ying-Hui],
Few-Shot Incremental Learning with Continually Evolved Classifiers,
CVPR21(12450-12459)
IEEE DOI 2111
Adaptation models, Machine learning algorithms, Training data, Benchmark testing, Power capacitors, Pattern recognition BibRef

Shukla, M.[Megh], Ahmed, S.[Shuaib],
A Mathematical Analysis of Learning Loss for Active Learning in Regression,
TCV21(3315-3323)
IEEE DOI 2109
Training, Industries, Fault diagnosis, Computational modeling, Pose estimation, Refining, Mathematical analysis BibRef

Rakesh, V.[Vineeth], Jain, S.[Swayambhoo],
Efficacy of Bayesian Neural Networks in Active Learning,
LLID21(2601-2609)
IEEE DOI 2109
Uncertainty, Monte Carlo methods, Neural networks, Estimation, Machine learning, Data models BibRef

Masana, M.[Marc], Tuytelaars, T.[Tinne], van de Weijer, J.[Joost],
Ternary Feature Masks: zero-forgetting for task-incremental learning,
CLVision21(3565-3574)
IEEE DOI 2109
Scalability, Encoding, Pattern recognition, Computational efficiency, Task analysis BibRef

van de Ven, G.M.[Gido M.], Li, Z.[Zhe], Tolias, A.S.[Andreas S.],
Class-Incremental Learning with Generative Classifiers,
CLVision21(3606-3615)
IEEE DOI 2109
Training, Learning systems, Deep learning, Monte Carlo methods, Benchmark testing BibRef

Sun, W.J.[Wen-Ju], Zhang, J.[Jing], Wang, D.Y.[Dan-Yu], Geng, Y.A.[Yangli-Ao], Li, Q.Y.[Qing-Yong],
ILCOC: An Incremental Learning Framework based on Contrastive One-class Classifiers,
CLVision21(3575-3583)
IEEE DOI 2109
Degradation, Heuristic algorithms, Computational modeling, Pattern recognition, Classification algorithms BibRef

Jiang, J.[Jian], Cetin, E.[Edoardo], Celiktutan, O.[Oya],
IB-DRR: Incremental Learning with Information-Back Discrete Representation Replay,
CLVision21(3528-3537)
IEEE DOI 2109
Training, Image coding, Memory management, Machine learning, Pattern recognition BibRef

Mittal, S.[Sudhanshu], Galesso, S.[Silvio], Brox, T.[Thomas],
Essentials for Class Incremental Learning,
CLVision21(3508-3517)
IEEE DOI 2109
Learning systems, Art, Neural networks, Training data, Boosting BibRef

Douillard, A.[Arthur], Valle, E.[Eduardo], Ollion, C.[Charles], Robert, T.[Thomas], Cord, M.[Matthieu],
Insights from the Future for Continual Learning,
CLVision21(3477-3486)
IEEE DOI 2109
Training, Computational modeling, Training data, Pattern recognition, Task analysis BibRef

Hayes, T.L.[Tyler L.], Kanan, C.[Christopher],
Selective Replay Enhances Learning in Online Continual Analogical Reasoning,
CLVision21(3497-3507)
IEEE DOI 2109
Measurement, Protocols, Neural networks, Reinforcement learning, Streaming media, Cognition, Pattern recognition BibRef

Kuo, N.I.H.[Nicholas I-Hsien], Harandi, M.[Mehrtash], Fourrier, N.[Nicolas], Walder, C.[Christian], Ferraro, G.[Gabriela], Suominen, H.[Hanna],
Plastic and Stable Gated Classifiers for Continual Learning,
CLVision21(3548-3553)
IEEE DOI 2109
Training, Knowledge engineering, Neural networks, Logic gates, Feature extraction, Robustness BibRef

Mai, Z.[Zheda], Li, R.[Ruiwen], Kim, H.W.[Hyun-Woo], Sanner, S.[Scott],
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning,
CLVision21(3584-3594)
IEEE DOI 2109
Training, Performance gain, Pattern recognition BibRef

Lomonaco, V.[Vincenzo], Pellegrini, L.[Lorenzo], Cossu, A.[Andrea], Carta, A.[Antonio], Graffieti, G.[Gabriele], Hayes, T.L.[Tyler L.], de Lange, M.[Matthias], Masana, M.[Marc], Pomponi, J.[Jary], van de Ven, G.M.[Gido M.], Mundt, M.[Martin], She, Q.[Qi], Cooper, K.[Keiland], Forest, J.[Jeremy], Belouadah, E.[Eden], Calderara, S.[Simone], Parisi, G.I.[German I.], Cuzzolin, F.[Fabio], Tolias, A.S.[Andreas S.], Scardapane, S.[Simone], Antiga, L.[Luca], Ahmad, S.[Subutai], Popescu, A.[Adrian], Kanan, C.[Christopher], van de Weijer, J.[Joost], Tuytelaars, T.[Tinne], Bacciu, D.[Davide], Maltoni, D.[Davide],
Avalanche: an End-to-End Library for Continual Learning,
CLVision21(3595-3605)
IEEE DOI 2109
Training, Deep learning, Machine learning algorithms, Collaboration, Libraries BibRef

Mirzadeh, S.I.[Seyed Iman], Ghasemzadeh, H.[Hassan],
CL-Gym: Full-Featured PyTorch Library for Continual Learning,
OmniCV21(3616-3622)
IEEE DOI 2109
Philosophical considerations, Learning (artificial intelligence), Libraries BibRef

Bagi, A.M.[Alexandra M.], Schild, K.I.[Kim I.], Khan, O.S.[Omar Shahbaz], Zahálka, J.[Jan], Jónsson, B.Þ.[Björn Þór],
XQM: Interactive Learning on Mobile Phones,
MMMod21(II:281-293).
Springer DOI 2106
BibRef

Shi, F.F.[Fei-Fei], Wang, P.[Peng], Shi, Z.C.[Zhong-Chao], Rui, Y.[Yong],
Selecting Useful Knowledge from Previous Tasks for Future Learning in a Single Network,
ICPR21(9727-9732)
IEEE DOI 2105
Knowledge engineering, Learning systems, Network architecture, Iterative methods, Task analysis BibRef

Jarboui, F.[Firas], Perchet, V.[Vianney],
Trajectory representation learning for Multi-Task NMRDP planning,
ICPR21(6786-6793)
IEEE DOI 2105
Non Markovian Reward Decision Processes. Bridges, Reinforcement learning, Markov processes, Trajectory, Planning, Task analysis BibRef

Lechat, A.[Alexis], Herbin, S.[Stéphane], Jurie, F.[Frédéric],
Semi-Supervised Class Incremental Learning,
ICPR21(10383-10389)
IEEE DOI 2105
Training, Protocols, Image reconstruction BibRef

Chang, X.Y.[Xin-Yuan], Tao, X.Y.[Xiao-Yu], Hong, X.P.[Xiao-Peng], Wei, X.[Xing], Ke, W.[Wei], Gong, Y.H.[Yi-Hong],
Class-Incremental Learning with Topological Schemas of Memory Spaces,
ICPR21(9719-9726)
IEEE DOI 2105
Multiprotocol label switching, Manifolds, Knowledge engineering, Adaptation models, Network topology, Neural networks, Topological Schemas Model BibRef

Pernici, F.[Federico], Bruni, M.[Matteo], Baecchi, C.[Claudio], Turchini, F.[Francesco], del Bimbo, A.[Alberto],
Class-incremental Learning with Pre-allocated Fixed Classifiers,
ICPR21(6259-6266)
IEEE DOI 2105
Training, Knowledge engineering, Neural networks, Standards, Faces BibRef

Buzzega, P.[Pietro], Boschini, M.[Matteo], Porrello, A.[Angelo], Calderara, S.[Simone],
Rethinking Experience Replay: a Bag of Tricks for Continual Learning,
ICPR21(2180-2187)
IEEE DOI 2105
Degradation, Neural networks, Proposals, Erbium, Standards BibRef

Fidalgo-Merino, R.[Raúl], Gabrielli, L.[Lorenzo], Checchi, E.[Enrico],
Leveraging Sequential Pattern Information for Active Learning from Sequential Data,
ICPR21(6957-6964)
IEEE DOI 2105
Training, Machine learning algorithms, Annotations, Databases, Manuals, Machine learning, Data models BibRef

Agarwal, A.[Arvind], Mujumdar, S.[Shashank], Gupta, N.[Nitin], Mehta, S.[Sameep],
Budgeted Batch Mode Active Learning with Generalized Cost and Utility Functions,
ICPR21(7692-7698)
IEEE DOI 2105
Learning systems, Training data, Cost function, Data models, Labeling BibRef

Herde, M.[Marek], Kottke, D.[Daniel], Huseljic, D.[Denis], Sick, B.[Bernhard],
Multi-Annotator Probabilistic Active Learning,
ICPR21(10281-10288)
IEEE DOI 2105
Training, Deep learning, Annotations, Computational modeling, Employment, Manuals, Gaussian processes BibRef

Arnavaz, K.[Kasra], Feragen, A.[Aasa], Krause, O.[Oswin], Loog, M.[Marco],
Bayesian Active Learning for Maximal Information Gain on Model Parameters,
ICPR21(10524-10531)
IEEE DOI 2105
Machine learning, Data models, Bayes methods, Logistics BibRef

Li, M.H.[Ming-Han], Liu, X.L.[Xia-Lei], van de Weijer, J.[Joost], Raducanu, B.[Bogdan],
Learning to Rank for Active Learning: A Listwise Approach,
ICPR21(5587-5594)
IEEE DOI 2105
Training, Measurement, Correlation, Prediction algorithms, Classification algorithms, Labeling BibRef

Siméoni, O.[Oriane], Budnik, M.[Mateusz], Avrithis, Y.[Yannis], Gravier, G.[Guillaume],
Rethinking deep active learning: Using unlabeled data at model training,
ICPR21(1220-1227)
IEEE DOI 2105
Training, Deep learning, Pipelines, Semisupervised learning, Data models, Image classification BibRef

Li, C.[Cheng], Rana, S.[Santu], Gill, A.[Andrew], Nguyen, D.[Dang], Gupta, S.I.[Sun-Il], Venkatesh, S.[Svetha],
Factor Screening using Bayesian Active Learning and Gaussian Process Meta-Modelling,
ICPR21(3288-3295)
IEEE DOI 2105
Gaussian processes, Length measurement, Entropy, Bayes methods, Kernel, Factor screening, Gaussian Process BibRef

Li, X.O.[Xia-Obin], Shan, L.[Lianlei], Li, M.[Minglong], Wang, W.Q.[Wei-Qiang],
Energy Minimum Regularization in Continual Learning,
ICPR21(6404-6409)
IEEE DOI 2105
Learning systems, Sensitivity, Animals, Solids, Minimization, Pattern recognition, Task analysis BibRef

Ho, C.H.[Chih-Hsing], Tsai, S.H.L.[Shang-Ho Lawrence],
RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning,
ICPR21(6680-6687)
IEEE DOI 2105
Training, Memory management, Training data, Approximation algorithms, Classification algorithms, Streaming Learning BibRef

Lei, C.H.[Cheng-Hsun], Chen, Y.H.[Yi-Hsin], Peng, W.H.[Wen-Hsiao], Chiu, W.C.[Wei-Chen],
Class-Incremental Learning with Rectified Feature-Graph Preservation,
ACCV20(VI:358-374).
Springer DOI 2103
Learn new classes as they arrive. BibRef

Chan, D.M.[David M.], Vijayanarasimhan, S.[Sudheendra], Ross, D.A.[David A.], Canny, J.F.[John F.],
Active Learning for Video Description with Cluster-regularized Ensemble Ranking,
ACCV20(V:443-459).
Springer DOI 2103
BibRef

Wang, S.[Shuo], Li, Y.X.[Yue-Xiang], Ma, K.[Kai], Ma, R.[Ruhui], Guan, H.B.[Hai-Bing], Zheng, Y.F.[Ye-Feng],
Dual Adversarial Network for Deep Active Learning,
ECCV20(XXIV:680-696).
Springer DOI 2012
BibRef

Lin, Z.[Zudi], Wei, D.L.[Dong-Lai], Jang, W.D.[Won-Dong], Zhou, S.[Siyan], Chen, X.P.[Xu-Peng], Wang, X.Y.[Xue-Ying], Schalek, R.[Richard], Berger, D.[Daniel], Matejek, B.[Brian], Kamentsky, L.[Lee], Peleg, A.[Adi], Haehn, D.[Daniel], Jones, T.[Thouis], Parag, T.[Toufiq], Lichtman, J.[Jeff], Pfister, H.[Hanspeter],
Two Stream Active Query Suggestion for Active Learning in Connectomics,
ECCV20(XVIII:103-120).
Springer DOI 2012
BibRef

Ebrahimi, S.[Sayna], Meier, F.[Franziska], Calandra, R.[Roberto], Darrell, T.J.[Trevor J.], Rohrbach, M.[Marcus],
Adversarial Continual Learning,
ECCV20(XI:386-402).
Springer DOI 2011
BibRef

Kim, C.D.[Chris Dongjoo], Jeong, J.[Jinseo], Kim, G.[Gunhee],
Imbalanced Continual Learning with Partitioning Reservoir Sampling,
ECCV20(XIII:411-428).
Springer DOI 2011
BibRef

Kim, E.S., Kim, J.U., Lee, S., Moon, S.K., Ro, Y.M.,
Class Incremental Learning With Task-Selection,
ICIP20(1846-1850)
IEEE DOI 2011
Task analysis, Learning systems, Image reconstruction, Feature extraction, Training, Testing, Data models, Deep learning, autoencoder BibRef

Gao, M.F.[Ming-Fei], Zhang, Z.Z.[Zi-Zhao], Yu, G.[Guo], Arik, S.Ö.[Sercan Ö.], Davis, L.S.[Larry S.], Pfister, T.[Tomas],
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost,
ECCV20(X:510-526).
Springer DOI 2011
BibRef

Chaplot, D.S.[Devendra Singh], Jiang, H.[Helen], Gupta, S.[Saurabh], Gupta, A.[Abhinav],
Semantic Curiosity for Active Visual Learning,
ECCV20(VI:309-326).
Springer DOI 2011
BibRef

Fini, E.[Enrico], Lathuilière, S.[Stéphane], Sangineto, E.[Enver], Nabi, M.[Moin], Ricci, E.[Elisa],
Online Continual Learning Under Extreme Memory Constraints,
ECCV20(XXVIII:720-735).
Springer DOI 2011
BibRef

Agarwal, S.[Sharat], Arora, H.[Himanshu], Anand, S.[Saket], Arora, C.[Chetan],
Contextual Diversity for Active Learning,
ECCV20(XVI: 137-153).
Springer DOI 2010
BibRef

Iscen, A.[Ahmet], Zhang, J.[Jeffrey], Lazebnik, S.[Svetlana], Schmid, C.[Cordelia],
Memory-efficient Incremental Learning Through Feature Adaptation,
ECCV20(XVI: 699-715).
Springer DOI 2010
BibRef

Yu, L., Twardowski, B., Liu, X., Herranz, L., Wang, K., Cheng, Y., Jui, S., van de Weijer, J.,
Semantic Drift Compensation for Class-Incremental Learning,
CVPR20(6980-6989)
IEEE DOI 2008
Task analysis, Training, Prototypes, Semantics, Measurement, Neurons BibRef

Zhao, B., Xiao, X., Gan, G., Zhang, B., Xia, S.,
Maintaining Discrimination and Fairness in Class Incremental Learning,
CVPR20(13205-13214)
IEEE DOI 2008
Training, Task analysis, Data models, Error analysis, Neural networks, Standards BibRef

Rajasegaran, J., Khan, S., Hayat, M., Khan, F.S., Shah, M.,
iTAML: An Incremental Task-Agnostic Meta-learning Approach,
CVPR20(13585-13594)
IEEE DOI 2008
Task analysis, Adaptation models, Training, Stability analysis, Interference, Predictive models, Heuristic algorithms BibRef

He, J., Mao, R., Shao, Z., Zhu, F.,
Incremental Learning in Online Scenario,
CVPR20(13923-13932)
IEEE DOI 2008
Data models, Machine learning, Training, Task analysis, Feature extraction, Predictive models, Learning systems BibRef

Mi, F., Kong, L., Lin, T., Yu, K., Faltings, B.,
Generalized Class Incremental Learning,
CLVision20(970-974)
IEEE DOI 2008
Erbium, Training, Data models, Computational modeling, Probabilistic logic, Machine learning, Task analysis BibRef

Ayub, A., Wagner, A.R.,
Cognitively-Inspired Model for Incremental Learning Using a Few Examples,
CLVision20(897-906)
IEEE DOI 2008
Feature extraction, Task analysis, Training, Machine learning, Training data, Data models, Hippocampus BibRef

Hayes, T.L., Kanan, C.,
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis,
CLVision20(887-896)
IEEE DOI 2008
Streaming media, Covariance matrices, Training, Computational modeling, Neural networks, Task analysis, Linear discriminant analysis BibRef

Dhar, P.[Prithviraj], Singh, R.V.[Rajat Vikram], Peng, K.C.[Kuan-Chuan], Wu, Z.[Ziyan], Chellappa, R.[Rama],
Learning Without Memorizing,
CVPR19(5133-5141).
IEEE DOI 2002
Incremental learning, but can't store the whole past. BibRef

Hou, S.H.[Sai-Hui], Pan, X.Y.[Xin-Yu], Loy, C.C.[Chen Change], Wang, Z.L.[Zi-Lei], Lin, D.H.[Da-Hua],
Learning a Unified Classifier Incrementally via Rebalancing,
CVPR19(831-839).
IEEE DOI 2002
BibRef

Belouadah, E.[Eden], Popescu, A.[Adrian],
DeeSIL: Deep-Shallow Incremental Learning,
TASKCV18(II:151-157).
Springer DOI 1905
BibRef

Castro, F.M.[Francisco M.], Marín-Jiménez, M.J.[Manuel J.], Guil, N.[Nicolás], Schmid, C.[Cordelia], Alahari, K.[Karteek],
End-to-End Incremental Learning,
ECCV18(XII: 241-257).
Springer DOI 1810
BibRef

Prabhu, A.[Ameya], Torr, P.H.S.[Philip H. S.], Dokania, P.K.[Puneet K.],
GDUMB: A Simple Approach that Questions Our Progress in Continual Learning,
ECCV20(II:524-540).
Springer DOI 2011
BibRef

Chaudhry, A.[Arslan], Dokania, P.K.[Puneet K.], Ajanthan, T.[Thalaiyasingam], Torr, P.H.S.[Philip H. S.],
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence,
ECCV18(XI: 556-572).
Springer DOI 1810
BibRef

Lomonaco, V., Maltoni, D., Pellegrini, L.,
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches,
CLVision20(989-998)
IEEE DOI 2008
Training, Robots, Videos, Object recognition, Benchmark testing, Computational modeling BibRef

Silver, D.L., Mahfuz, S.,
Generating Accurate Pseudo Examples for Continual Learning,
CLVision20(1035-1042)
IEEE DOI 2008
Task analysis, Training, Probability distribution, Knowledge engineering, Input variables, Neural networks BibRef

Parshotam, K., Kilickaya, M.,
Continual Learning of Object Instances,
CLVision20(907-914)
IEEE DOI 2008
Automobiles, Task analysis, Measurement, Training, Data models, Visualization, Companies BibRef

Liu, X., Wu, C., Menta, M., Herranz, L., Raducanu, B., Bagdanov, A.D., Jui, S., van de Weijer, J.,
Generative Feature Replay For Class-Incremental Learning,
CLVision20(915-924)
IEEE DOI 2008
Task analysis, Feature extraction, Image generation, Correlation, Training, Generators BibRef

Mirzadeh, S.I., Farajtabar, M., Ghasemzadeh, H.,
Dropout as an Implicit Gating Mechanism For Continual Learning,
CLVision20(945-951)
IEEE DOI 2008
Task analysis, Neurons, Stability analysis, Training, Standards, Logic gates, Knowledge engineering BibRef

Liu, Y., Su, Y., Liu, A., Schiele, B., Sun, Q.,
Mnemonics Training: Multi-Class Incremental Learning Without Forgetting,
CVPR20(12242-12251)
IEEE DOI 2008
Training, Optimization, Data models, Computational modeling, Generative adversarial networks, Training data BibRef

Zhang, J.[Jie], Zhang, J.T.[Jun-Ting], Ghosh, S.[Shalini], Li, D.W.[Da-Wei], Zhu, J.W.[Jing-Wen], Zhang, H.M.[He-Ming], Wang, Y.L.[Ya-Lin],
Regularize, Expand and Compress: NonExpansive Continual Learning,
WACV20(843-851)
IEEE DOI 2006
Task analysis, Computational modeling, Network architecture, Neural networks, Knowledge engineering, Correlation BibRef

Slim, H.[Habib], Belouadah, E.[Eden], Popescu, A.[Adrian], Onchis, D.[Darian],
Dataset Knowledge Transfer for Class-Incremental Learning without Memory,
WACV22(3311-3320)
IEEE DOI 2202
Training, Deep learning, Design methodology, Memory management, Neural networks, Semi- and Un- supervised Learning BibRef

Belouadah, E.[Eden], Popescu, A.[Adrian],
ScaIL: Classifier Weights Scaling for Class Incremental Learning,
WACV20(1255-1264)
IEEE DOI 2006
BibRef
Earlier:
IL2M: Class Incremental Learning With Dual Memory,
ICCV19(583-592)
IEEE DOI 2004
Tuning, Adaptation models, Training, Feature extraction, Machine learning, Memory management, Task analysis. computational complexity, image classification, inference mechanisms, learning (artificial intelligence), Computer architecture BibRef

Ostapenko, O.[Oleksiy], Puscas, M.[Mihai], Klein, T.[Tassilo], Jahnichen, P.[Patrick], Nabi, M.[Moin],
Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning,
CVPR19(11313-11321).
IEEE DOI 2002
BibRef

Stojanov, S.[Stefan], Mishra, S.[Samarth], Thai, N.A.[Ngoc Anh], Dhanda, N.[Nikhil], Humayun, A.[Ahmad], Yu, C.[Chen], Smith, L.B.[Linda B.], Rehg, J.M.[James M.],
Incremental Object Learning From Contiguous Views,
CVPR19(8769-8778).
IEEE DOI 2002
BibRef

Murata, K.[Kengo], Toyota, T.[Tetsuya], Ohara, K.[Kouzou],
What is Happening Inside a Continual Learning Model?: A Representation-Based Evaluation of Representational Forgetting,
CLVision20(952-956)
IEEE DOI 2008
Task analysis, Erbium, Measurement, Learning systems, Standards, Neural networks, Data models BibRef

Abati, D., Tomczak, J., Blankevoort, T., Calderara, S., Cucchiara, R., Bejnordi, B.E.,
Conditional Channel Gated Networks for Task-Aware Continual Learning,
CVPR20(3930-3939)
IEEE DOI 2008
Task analysis, Logic gates, Training, Computational modeling, Neural networks, Machine learning, Computer architecture BibRef

Lee, J., Hong, H.G., Joo, D., Kim, J.,
Continual Learning With Extended Kronecker-Factored Approximate Curvature,
CVPR20(8998-9007)
IEEE DOI 2008
Task analysis, Neural networks, Mathematical model, Learning systems, Optimization, Network architecture, Training BibRef

Kim, J., Kim, J., Kwak, N.,
StackNet: Stacking feature maps for Continual learning,
CLVision20(975-982)
IEEE DOI 2008
Task analysis, Indexes, Training, Data models, Biological neural networks, Stacking, Machine learning BibRef

Du, X., Li, Z., Seo, J., Liu, F., Cao, Y.,
Noise-based Selection of Robust Inherited Model for Accurate Continual Learning,
CLVision20(983-988)
IEEE DOI 2008
Pattern recognition BibRef

Lomonaco, V., Desai, K., Culurciello, E., Maltoni, D.,
Continual Reinforcement Learning in 3D Non-stationary Environments,
CLVision20(999-1008)
IEEE DOI 2008
Task analysis, Learning (artificial intelligence), Benchmark testing, Color, Training, Complexity theory BibRef

Aljundi, R.[Rahaf], Kelchtermans, K.[Klaas], Tuytelaars, T.[Tinne],
Task-Free Continual Learning,
CVPR19(11246-11255).
IEEE DOI 2002
BibRef

Park, D.M.[Dong-Min], Hong, S.[Seokil], Han, B.H.[Bo-Hyung], Lee, K.M.[Kyoung Mu],
Continual Learning by Asymmetric Loss Approximation With Single-Side Overestimation,
ICCV19(3334-3343)
IEEE DOI 2004
function approximation, learning (artificial intelligence), neural nets, asymmetric loss approximation, Scalability BibRef

El Khatib, A.[Alaa], Karray, F.[Fakhri],
Strategies for Improving Single-Head Continual Learning Performance,
ICIAR19(I:452-460).
Springer DOI 1909
Forgetting. Problem is also not all data is available at once. BibRef

Hayes, T.L., Kemker, R., Cahill, N.D., Kanan, C.,
New Metrics and Experimental Paradigms for Continual Learning,
DeepLearnRV18(2112-21123)
IEEE DOI 1812
Robots, Measurement, Training, Task analysis, Computational modeling, Neural networks, Data models BibRef

Zhai, M.Y.[Meng-Yao], Chen, L.[Lei], Mori, G.[Greg],
Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation,
CVPR21(2246-2255)
IEEE DOI 2111
Deep learning, Costs, Heuristic algorithms, Memory management, Filtering algorithms, Information filters, Generators BibRef

Zhai, M.Y.[Meng-Yao], Chen, L.[Lei], He, J.W.[Jia-Wei], Nawhal, M.[Megha], Tung, F.[Frederick], Mori, G.[Greg],
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation,
ECCV20(XXI:397-413).
Springer DOI 2011
BibRef
Earlier: A1, A2, A5, A3, A4, A6:
Lifelong GAN: Continual Learning for Conditional Image Generation,
ICCV19(2759-2768)
IEEE DOI 2004
image processing, learning (artificial intelligence), neural nets, continual learning, deep neural networks, Training data BibRef

Lopes, N.[Noel], Ribeiro, B.[Bernardete],
Trading off Distance Metrics vs Accuracy in Incremental Learning Algorithms,
CIARP16(530-538).
Springer DOI 1703
BibRef
Earlier:
On the Impact of Distance Metrics in Instance-Based Learning Algorithms,
IbPRIA15(48-56).
Springer DOI 1506
BibRef

Ditzler, G.[Gregory], Polikar, R.[Robi], Chawla, N.V.[Nitesh V.],
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance,
ICPR10(2997-3000).
IEEE DOI 1008
BibRef

Almaksour, A.[Abdullah], Anquetil, E.[Eric], Quiniou, S.[Solen], Cheriet, M.[Mohamed],
Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems,
ICPR10(4056-4059).
IEEE DOI 1008
BibRef

Sudo, K.[Kyoko], Osawa, T.[Tatsuya], Tanaka, H.[Hidenori], Koike, H.[Hideki], Arakawa, K.[Kenichi],
Online anomal movement detection based on unsupervised incremental learning,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhang, R.[Rong], Rudnicky, A.I.[Alexander I.],
A New Data Selection Principle for Semi-Supervised Incremental Learning,
ICPR06(II: 780-783).
IEEE DOI 0609
BibRef

Prehn, H.[Herward], Sommer, G.[Gerald],
An Adaptive Classification Algorithm Using Robust Incremental Clustering,
ICPR06(I: 896-899).
IEEE DOI 0609
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Subspace Clustering, Subspace Learning .