14.2.6.1 Dynamic Learning, Incremental Learning

Chapter Contents (Back)
Dynamic Learning. Incremental Learning. Online learning. Forgetting is one issue.
See also Continual Learning.
See also Active Learning.
See also Intrepretation, Explaination, Understanding of Convolutional Neural Networks.
See also Forgetting, Learning without Forgetting, Convolutional Neural Networks.
See also Reinforcement 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

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

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

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

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.C.[You-Cheng], Zhou, J.[Jin], Wang, Y.[Yan], Sun, X.S.[Xiao-Shuai], Zhu, P.F.[Peng-Fei], Wu, C.L.[Cheng-Lin], 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

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

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

Zhou, S.[Shiji], Wang, L.[Lianzhe], Zhang, S.H.[Shang-Hang], Wang, Z.[Zhi], Zhu, W.W.[Wen-Wu],
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

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

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

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

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], Hou, Z.S.[Zhi-Shen], 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

Liu, B.H.[Bing-Hao], Yang, B.[Boyu], Xie, L.X.[Ling-Xi], Wang, R.[Ren], Tian, Q.[Qi], Ye, Q.X.[Qi-Xiang],
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning,
PAMI(45), No. 10, October 2023, pp. 12699-12706.
IEEE DOI 2310
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

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.W.[Wen-Wu],
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

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

Biondi, N.[Niccolò], Pernici, F.[Federico], Bruni, M.[Matteo], del imbo, A.[Alberto],
CoReS: Compatible Representations via Stationarity,
PAMI(45), No. 8, August 2023, pp. 9567-9582.
IEEE DOI 2307
Update with new data. Feature extraction, Training, Representation learning, Data models, Visualization, Prototypes, Network architecture, representation learning BibRef

Hadikhani, P.[Parham], Lai, D.T.C.[Daphne Teck Ching], Ong, W.H.[Wee-Hong], Nadimi-Shahraki, M.H.[Mohammad H.],
Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution clustering with manifold learning and incremental technique,
IVC(136), 2023, pp. 104712.
Elsevier DOI 2308
Unsupervised learning, Deep clustering, Feature extraction, Dimension reduction, Image clustering, Evolutionary algorithm, Auto-encoder BibRef

Shi, L.[Lei], Zhao, K.[Kai], Fu, Z.[Zhenyong],
Boosting separated softmax with discrimination for class incremental learning,
JVCIR(95), 2023, pp. 103899.
Elsevier DOI 2309
Incremental learning, Discrimination enhancement, Discriminative separated softmax BibRef

Yang, S.J.[Shuo-Jin], Cai, Z.C.[Zhan-Chuan],
Cross Domain Lifelong Learning Based on Task Similarity,
PAMI(45), No. 10, October 2023, pp. 11612-11623.
IEEE DOI 2310
BibRef

Li, J.[Jing], Pan, Y.[Yuangang], Lyu, Y.M.[Yue-Ming], Yao, Y.H.[Ying-Hua], Sui, Y.[Yulei], Tsang, I.W.[Ivor W.],
Earning Extra Performance From Restrictive Feedbacks,
PAMI(45), No. 10, October 2023, pp. 11753-11765.
IEEE DOI 2310
BibRef

Wang, S.[Shaokun], Shi, W.W.[Wei-Wei], Dong, S.[Songlin], Gao, X.Y.[Xin-Yuan], Song, X.[Xiang], Gong, Y.H.[Yi-Hong],
Semantic Knowledge Guided Class-Incremental Learning,
CirSysVideo(33), No. 10, October 2023, pp. 5921-5931.
IEEE DOI 2310
BibRef

Hou, C.P.[Chen-Ping], Gu, S.L.[Shi-Lin], Xu, C.[Chao], Qian, Y.H.[Yu-Hua],
Incremental Learning for Simultaneous Augmentation of Feature and Class,
PAMI(45), No. 12, December 2023, pp. 14789-14806.
IEEE DOI 2311
BibRef

Ni, H.T.[Hao-Tian], Gu, S.L.[Shi-Lin], Fan, R.D.[Rui-Dong], Hou, C.P.[Chen-Ping],
Feature incremental learning with causality,
PR(146), 2024, pp. 110033.
Elsevier DOI 2311
Feature incremental, Causal inference, Balancing regularizer BibRef

Kolouri, S.[Soheil], Abbasi, A.[Ali], Koohpayegani, S.A.[Soroush Abbasi], Nooralinejad, P.[Parsa], Pirsiavash, H.[Hamed],
Multi-Agent Lifelong Implicit Neural Learning,
SPLetters(30), 2023, pp. 1812-1816.
IEEE DOI 2312
BibRef

Zeng, L.B.[Long-Bin], Han, J.Y.[Jia-Yi], Du, L.[Liang], Ding, W.Y.[Wei-Yang],
Rethinking precision of pseudo label: Test-time adaptation via complementary learning,
PRL(177), 2024, pp. 96-102.
Elsevier DOI 2401
Test-time adaptation, Complementary label, Unsupervised learning BibRef

Wei, K.[Kun], Yang, X.[Xu], Xu, Z.[Zhe], Deng, C.[Cheng],
Class-Incremental Unsupervised Domain Adaptation via Pseudo-Label Distillation,
IP(33), 2024, pp. 1188-1198.
IEEE DOI 2402
Adaptation models, Training, Feature extraction, Information filters, Task analysis, Prototypes, Data models, pseudo-label distillation BibRef

Zhao, H.B.[Han-Bin], Fu, Y.J.[Yong-Jian], Kang, M.T.[Min-Tong], Tian, Q.[Qi], Wu, F.[Fei], Li, X.[Xi],
MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning,
PAMI(46), No. 3, March 2024, pp. 1576-1588.
IEEE DOI 2402
Task analysis, Power capacitors, Knowledge engineering, Training, Frequency-domain analysis, Extraterrestrial measurements, class-incremental learning BibRef

Ur Rahman, M.E.[Mohammed Ehsan], Ahmad, I.S.[Imran Shafiq],
Quantitative analysis of transfer and incremental learning for image classification,
IJCVR(14), No. 2, 2024, pp. 202-212.
DOI Link 2403
BibRef

He, C.[Chen], Wang, R.P.[Rui-Ping], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin],
Introspective GAN: Learning to grow a GAN for incremental generation and classification,
PR(151), 2024, pp. 110383.
Elsevier DOI Code:
WWW Link. 2404
Incremental learning, Catastrophic forgetting, Generative Adversarial Networks BibRef

Liu, C.[Chong], Wang, Y.[Yi], Li, D.[Dong], Wang, X.[Xizhao],
Domain-incremental learning without forgetting based on random vector functional link networks,
PR(151), 2024, pp. 110430.
Elsevier DOI 2404
Incremental learning, Domain-incremental learning, RVFL network, Catastrophic forgetting, Privacy preservation BibRef

Jiang, M.[Mudi], Hu, L.[Lianyu], Han, X.[Xin], Zhou, Y.[Yong], He, Z.Y.[Zeng-You],
A randomized algorithm for clustering discrete sequences,
PR(151), 2024, pp. 110388.
Elsevier DOI 2404
Sequence clustering, Sequential data analysis, Cluster analysis, Randomized algorithm BibRef

Ma, B.[Bingtao], Cong, Y.[Yang], Ren, Y.[Yu],
IOSL: Incremental Open Set Learning,
CirSysVideo(34), No. 4, April 2024, pp. 2235-2248.
IEEE DOI 2404
Task analysis, Training, Prototypes, Robots, Adaptation models, Feature extraction, Extraterrestrial measurements, class incremental learning BibRef

Wu, R.[Ran], Liu, H.Y.[Huan-Yu], Yue, Z.[Zongcheng], Li, J.B.[Jun-Bao], Sham, C.W.[Chiu-Wing],
Hyper-feature aggregation and relaxed distillation for class incremental learning,
PR(152), 2024, pp. 110440.
Elsevier DOI 2405
Class incremental learning, Relaxed knowledge distillation, Hyper-feature aggregation BibRef

Zhu, J.[Jitao], Luo, G.[Guibo], Duan, B.[Baishan], Zhu, Y.S.[Yue-Sheng],
Class Incremental Learning With Deep Contrastive Learning and Attention Distillation,
SPLetters(31), 2024, pp. 1224-1228.
IEEE DOI 2405
Task analysis, Feature extraction, Self-supervised learning, Data models, Stability criteria, Training, Image classification, knowledge distillation BibRef

Song, J.[Jialun], Chen, J.[Jian], Du, L.[Lan],
Rebalancing network with knowledge stability for class incremental learning,
PR(153), 2024, pp. 110506.
Elsevier DOI 2405
Class incremental learning, Catastrophic forgetting, Class imbalance, Proxy-based metric learning, Knowledge distillation BibRef

Feng, Z.K.[Zhi-Kun], Zhou, M.[Mian], Gao, Z.[Zan], Stefanidis, A.[Angelos], Su, J.L.[Jiong-Long], Dang, K.[Kang], Li, C.[Chuanhui],
Adaptive knowledge transfer for class incremental learning,
PRL(183), 2024, pp. 165-171.
Elsevier DOI 2406
Class incremental learning, Knowledge sharing, Knowledge distillation, Dynamic network BibRef

Su, Y.Y.[Yong-Yi], Xu, X.[Xun], Li, T.R.[Tian-Rui], Jia, K.[Kui],
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training,
PAMI(46), No. 8, August 2024, pp. 5524-5540.
IEEE DOI 2407
Training, Adaptation models, Protocols, Data models, Predictive models, Training data, Streaming media, self-training BibRef


Deng, Y.[Yao], Xiang, X.[Xiang],
Expanding Hyperspherical Space for Few-Shot Class-Incremental Learning,
WACV24(1956-1965)
IEEE DOI 2404
Prototypes, Benchmark testing, Data models, Power capacitors, Task analysis, Algorithms, Machine learning architectures, Image recognition and understanding BibRef

Tan, Y.[Yuwen], Xiang, X.[Xiang],
Cross-Domain Few-Shot Incremental Learning for Point-Cloud Recognition,
WACV24(2296-2305)
IEEE DOI 2404
Adaptation models, Robot sensing systems, Power capacitors, Sensors, Object recognition, Algorithms, Image recognition and understanding BibRef

Kim, S.[Solang], Jeong, Y.[Yuho], Park, J.S.[Joon Sung], Yoon, S.W.[Sung Whan],
MICS: Midpoint Interpolation to Learn Compact and Separated Representations for Few-Shot Class-Incremental Learning,
WACV24(2225-2234)
IEEE DOI Code:
WWW Link. 2404
Training, Microwave integrated circuits, Interpolation, Codes, Computational modeling, Benchmark testing, Algorithms BibRef

Liu, Y.Y.[Yao-Yao], Li, Y.Y.[Ying-Ying], Schiele, B.[Bernt], Sun, Q.[Qianru],
Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos,
WACV24(2215-2224)
IEEE DOI 2404
Training, Learning systems, Adaptation models, Costs, Markov decision processes, Memory management, Streaming media, Image recognition and understanding BibRef

Li, S.[Shiyao], Ning, X.F.[Xue-Fei], Zhang, S.H.[Shang-Hang], Guo, L.[Lidong], Zhao, T.C.[Tian-Chen], Yang, H.Z.[Hua-Zhong], Wang, Y.[Yu],
TCP: Triplet Contrastive-relationship Preserving for Class-Incremental Learning,
WACV24(2020-2029)
IEEE DOI 2404
Self-supervised learning, Artificial neural networks, Algorithms, Machine learning architectures, formulations, and algorithms BibRef

Roy, S.[Soumya], Verma, V.[Vinay], Gupta, D.[Deepak],
Efficient Expansion and Gradient Based Task Inference for Replay Free Incremental Learning,
WACV24(1154-1164)
IEEE DOI 2404
Adaptation models, Transfer learning, Predictive models, Information filters, Data augmentation, Entropy, Data models BibRef

Li, Y.S.[Yu-Shu], Xu, X.[Xun], Su, Y.Y.[Yong-Yi], Jia, K.[Kui],
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion,
ICCV23(11802-11812)
IEEE DOI Code:
WWW Link. 2401
BibRef

Hakim, G.A.V.[Gustavo A. Vargas], Osowiechi, D.[David], Noori, M.[Mehrdad], Cheraghalikhani, M.[Milad], Bahri, A.[Ali], Ben Ayed, I.[Ismail], Desrosiers, C.[Christian],
ClusT3: Information Invariant Test-Time Training,
ICCV23(6113-6112)
IEEE DOI Code:
WWW Link. 2401
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

Shi, W.[Wuxuan], Ye, M.[Mang],
Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation for Non-Exemplar Class-Incremental Learning,
ICCV23(1772-1781)
IEEE DOI 2401
BibRef

Tang, Y.M.[Yu-Ming], Peng, Y.X.[Yi-Xing], Zheng, W.S.[Wei-Shi],
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining,
ICCV23(1706-1716)
IEEE DOI Code:
WWW Link. 2401
BibRef

Pei, Y.X.[Yi-Xuan], Qing, Z.W.[Zhi-Wu], Zhang, S.W.[Shi-Wei], Wang, X.[Xiang], Zhang, Y.[Yingya], Zhao, D.L.[De-Li], Qian, X.M.[Xue-Ming],
Space-time Prompting for Video Class-incremental Learning,
ICCV23(11898-11908)
IEEE DOI 2401
BibRef

Dong, J.H.[Jia-Hua], Liang, W.Q.[Wen-Qi], Cong, Y.[Yang], Sun, G.[Gan],
Heterogeneous Forgetting Compensation for Class-Incremental Learning,
ICCV23(11708-11717)
IEEE DOI Code:
WWW Link. 2401
BibRef

Moon, J.Y.[Jun-Yeong], Park, K.H.[Keon-Hee], Kim, J.U.[Jung Uk], Park, G.M.[Gyeong-Moon],
Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning,
ICCV23(11697-11707)
IEEE DOI Code:
WWW Link. 2401
BibRef

Panos, A.[Aristeidis], Kobe, Y.[Yuriko], Reino, D.O.[Daniel Olmeda], Aljundi, R.[Rahaf], Turner, R.E.[Richard E.],
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning,
ICCV23(18774-18784)
IEEE DOI 2401
BibRef

Chen, X.W.[Xiu-Wei], Chang, X.B.[Xia-Bin],
Dynamic Residual Classifier for Class Incremental Learning,
ICCV23(18697-18706)
IEEE DOI 2401
BibRef

Psaltis, A.[Athanasios], Chatzikonstantinou, C.[Christos], Patrikakis, C.Z.[Charalampos Z.], Daras, P.[Petros],
FedRCIL: Federated Knowledge Distillation for Representation based Contrastive Incremental Learning,
VCL23(3455-3464)
IEEE DOI 2401
BibRef

Kanagarajah, S.[Sathursan], Ambegoda, T.[Thanuja], Rodrigo, R.[Ranga],
SATHUR: Self Augmenting Task Hallucinal Unified Representation for Generalized Class Incremental Learning,
VCL23(3465-3472)
IEEE DOI 2401
BibRef

Guo, C.X.[Chen-Xu], Zhao, Q.[Qi], Lyu, S.C.[Shu-Chang], Liu, B.[Binghao], Wang, C.L.[Chun-Lei], Chen, L.[Lijiang], Cheng, G.L.[Guang-Liang],
Decision Boundary Optimization for Few-shot Class-Incremental Learning,
VCL23(3493-3503)
IEEE DOI 2401
BibRef

Xiang, J.L.[Jin-Lin], Shlizerman, E.[Eli],
TKIL: Tangent Kernel Optimization for Class Balanced Incremental Learning,
VCL23(3521-3531)
IEEE DOI 2401
BibRef

Jodelet, Q.[Quentin], Liu, X.[Xin], Phua, Y.J.[Yin Jun], Murata, T.[Tsuyoshi],
Class-Incremental Learning using Diffusion Model for Distillation and Replay,
VCL23(3417-3425)
IEEE DOI 2401
BibRef

Lamers, C.[Christiaan], Vidal, R.[René], Belbachir, N.[Nabil], van Stein, N.[Niki], Bäck, T.[Thomas], Giampouras, P.[Paris],
Clustering-based Domain-Incremental Learning,
VCL23(3376-3384)
IEEE DOI 2401
BibRef

D'Alessandro, M.[Marco], Alonso, A.[Alberto], Calabrés, E.[Enrique], Galar, M.[Mikel],
Multimodal Parameter-Efficient Few-Shot Class Incremental Learning,
VCL23(3385-3395)
IEEE DOI 2401
BibRef

Xu, J.[Jiawen], Grohnfeldt, C.[Claas], Kao, O.[Odej],
OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning,
VCL23(3295-3303)
IEEE DOI 2401
BibRef

Zhao, Y.L.[Yun-Long], Deng, X.H.[Xiao-Heng], Pei, X.J.[Xin-Jun], Chen, X.C.[Xue-Chen], Li, D.[Deng],
Parallel Gradient Blend for Class Incremental Learning,
ICIP23(1220-1224)
IEEE DOI 2312
BibRef

Mutlu, O.C.[Onur Cezmi], Honarmand, M.[Mohammadmahdi], Surabhi, S.[Saimourya], Wall, D.P.[Dennis P.],
TempT: Temporal consistency for Test-time adaptation,
ABAW23(5917-5923)
IEEE DOI 2309
BibRef

Srivastava, S.[Shikhar], Yaqub, M.[Mohammad], Nandakumar, K.[Karthik],
Lifelong Learning of Task-Parameter Relationships for Knowledge Transfer,
CLVision23(2525-2534)
IEEE DOI 2309
BibRef

Zancato, L.[Luca], Achille, A.[Alessandro], Liu, T.Y.[Tian Yu], Trager, M.[Matthew], Perera, P.[Pramuditha], Soatto, S.[Stefano],
Train/Test-Time Adaptation with Retrieval,
CVPR23(15911-15921)
IEEE DOI 2309
BibRef

Yuan, L.[Longhui], Xie, B.[Binhui], Li, S.[Shuang],
Robust Test-Time Adaptation in Dynamic Scenarios,
CVPR23(15922-15932)
IEEE DOI 2309
BibRef

Daniali, M.[Maryam], Kim, E.[Edward],
Perception Over Time: Temporal Dynamics for Robust Image Understanding,
WiCV23(5656-5665)
IEEE DOI 2309
BibRef

Tang, Y.S.[Yu-Shun], Zhang, C.[Ce], Xu, H.[Heng], Chen, S.S.[Shuo-Shuo], Cheng, J.[Jie], Leng, L.[Luziwei], Guo, Q.H.[Qing-Hai], He, Z.H.[Zhi-Hai],
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation,
CVPR23(3728-3738)
IEEE DOI 2309
BibRef

Wang, W.J.[Wen-Jin], Hu, Y.Q.[Yun-Qing], Chen, Q.[Qianglong], Zhang, Y.[Yin],
Task Difficulty Aware Parameter Allocation and Regularization for Lifelong Learning,
CVPR23(7776-7785)
IEEE DOI 2309
BibRef

Song, Z.[Zeyin], Zhao, Y.F.[Yi-Fan], Shi, Y.J.[Yu-Jun], Peng, P.X.[Pei-Xi], Yuan, L.[Li], Tian, Y.H.[Yong-Hong],
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning,
CVPR23(24183-24192)
IEEE DOI 2309
BibRef

Song, X.[Xiang], Shu, K.[Kuang], Dong, S.[Songlin], Cheng, J.[Jie], Wei, X.[Xing], Gong, Y.H.[Yi-Hong],
Overcoming Catastrophic Forgetting for Multi-Label Class-Incremental Learning,
WACV24(2378-2387)
IEEE DOI 2404
Adaptation models, Decoding, Algorithms, Machine learning architectures, formulations, and algorithms, Image recognition and understanding BibRef

Dong, S.[Songlin], Luo, H.Y.[Hao-Yu], He, Y.H.[Yu-Hang], Wei, X.[Xing], Cheng, J.[Jie], Gong, Y.H.[Yi-Hong],
Knowledge Restore and Transfer for Multi-Label Class-Incremental Learning,
ICCV23(18665-18674)
IEEE DOI Code:
WWW Link. 2401
BibRef

Gao, X.Y.[Xin-Yuan], He, Y.H.[Yu-Hang], Dong, S.[Songlin], Cheng, J.[Jie], Wei, X.[Xing], Gong, Y.H.[Yi-Hong],
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning,
CVPR23(24236-24245)
IEEE DOI 2309
BibRef

Yu, X.F.[Xiao-Fan], Guo, Y.H.[Yun-Hui], Gao, S.[Sicun], Rosing, T.[Tajana],
SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge,
CLVision23(2484-2495)
IEEE DOI 2309
BibRef

Cai, T.[Tenghao], Zhang, Z.Z.[Zhi-Zhong], Tan, X.[Xin], Qu, Y.[Yanyun], Jiang, G.[Guannan], Wang, C.J.[Cheng-Jie], Xie, Y.[Yuan],
Multi-Centroid Task Descriptor for Dynamic Class Incremental Inference,
CVPR23(7298-7307)
IEEE DOI 2309
BibRef

Zhuang, H.P.[Hui-Ping], Weng, Z.Y.[Zhen-Yu], He, R.[Run], Lin, Z.P.[Zhi-Ping], Zeng, Z.Q.[Zi-Qian],
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task,
CVPR23(7746-7755)
IEEE DOI 2309
BibRef

Zhao, L.[Linglan], Lu, J.[Jing], Xu, Y.L.[Yun-Lu], Cheng, Z.Z.[Zhan-Zhan], Guo, D.[Dashan], Niu, Y.[Yi], Fang, X.Z.[Xiang-Zhong],
Few-Shot Class-Incremental Learning via Class-Aware Bilateral Distillation,
CVPR23(11838-11847)
IEEE DOI 2309
BibRef

Hu, Z.Y.[Zhi-Yuan], Li, Y.S.[Yun-Sheng], Lyu, J.C.[Jian-Cheng], Gao, D.[Dashan], Vasconcelos, N.M.[Nuno M.],
Dense Network Expansion for Class Incremental Learning,
CVPR23(11858-11867)
IEEE DOI 2309
BibRef

Cha, S.M.[Sung-Min], Ko, N.[Naeun], Choi, H.[Heewoong], Yoo, Y.J.[Young-Joon], Moon, T.[Taesup],
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations,
WACV24(3777-3787)
IEEE DOI 2404
Training, Smoothing methods, Perturbation methods, Noise, Closed box, Robustness, Internet, Algorithms, Adversarial learning, Low-level and physics-based vision BibRef

Cha, S.M.[Sung-Min], Cho, S.J.[Sung-Jun], Hwang, D.[Dasol], Hong, S.[Sunwon], Lee, M.[Moontae], Moon, T.[Taesup],
Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning,
CVPR23(20127-20136)
IEEE DOI 2309
BibRef

Sun, W.J.[Wen-Ju], Li, Q.Y.[Qing-Yong], Zhang, J.[Jing], Wang, W.[Wen], Geng, Y.A.[Yangli-Ao],
Decoupling Learning and Remembering: a Bilevel Memory Framework with Knowledge Projection for Task-Incremental Learning,
CVPR23(20186-20195)
IEEE DOI 2309
BibRef

Kim, D.[Dongwan], Han, B.H.[Bo-Hyung],
On the Stability-Plasticity Dilemma of Class-Incremental Learning,
CVPR23(20196-20204)
IEEE DOI 2309
BibRef

Kilickaya, M.[Mert], Vanschoren, J.[Joaquin],
Are Labels Needed for Incremental Instance Learning?,
CLVision23(2401-2409)
IEEE DOI 2309
BibRef

Mohamed, A.[Abdelrahman], Grandhe, R.[Rushali], Joseph, K.J.[K J], Khan, S.[Salman], Khan, F.[Fahad],
D3Former: Debiased Dual Distilled Transformer for Incremental Learning,
CLVision23(2421-2430)
IEEE DOI 2309
BibRef

Murata, K.[Kengo], Ito, S.[Seiya], Ohara, K.[Kouzou],
Learning and Transforming General Representations to Break Down Stability-plasticity Dilemma,
ACCV22(VI:544-560).
Springer DOI 2307
BibRef

Cai, C.Y.[Cheng-Yi], Liu, J.X.[Jia-Xin], Yu, W.[Wendi], Guo, Y.C.[Yu-Chen],
CLUE: Consolidating Learned and Undegroing Experience in Domain-incremental Classification,
ACCV22(V:281-296).
Springer DOI 2307
BibRef

Parga, C.D.[César D.], Vilariño, G.[Gabriel], Pardo, X.M.[Xosé M.], Regueiro, C.V.[Carlos V.],
S2-LOR: Supervised Stream Learning for Object Recognition,
IbPRIA23(300-311).
Springer DOI 2307
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], Soumm, M.[Michael], Feillet, E.[Eva], Popescu, A.[Adrian], Delezoide, B.[Bertrand], Picard, D.[David], Hudelot, C.[Céline],
An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning,
WACV24(1826-1836)
IEEE DOI 2404
Training, Statistical analysis, Transfer learning, Data models, Stability analysis, Classification algorithms, Algorithms, Embedded sensing / real-time techniques 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

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

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

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

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

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

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

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

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

Zhu, K.[Kai], Zheng, K.C.[Ke-Cheng], Feng, R.L.[Rui-Li], Zhao, D.L.[De-Li], Cao, Y.[Yang], Zha, Z.J.[Zheng-Jun],
Self-Organizing Pathway Expansion for Non-Exemplar Class-Incremental Learning,
ICCV23(19147-19156)
IEEE DOI 2401
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

Shi, Y.J.[Yu-Jun], Zhou, K.Q.[Kuang-Qi], Liang, J.[Jian], Jiang, Z.H.[Zi-Hang], 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

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

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

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], 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

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

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

Luo, Z.[Zilin], Liu, Y.Y.[Yao-Yao], Schiele, B.[Bernt], Sun, Q.[Qianru],
Class-Incremental Exemplar Compression for Class-Incremental Learning,
CVPR23(11371-11380)
IEEE DOI 2309
BibRef
Earlier: A2, A3, A4, Only:
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.T.[Xin-Ting], Tang, K.H.[Kai-Hua], Miao, C.Y.[Chun-Yan], Hua, X.S.[Xian-Sheng], Zhang, H.W.[Han-Wang],
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

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

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

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

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

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

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

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.Y.[Zi-Yan], 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

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

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

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

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

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

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
Continual Learning .


Last update:Jul 13, 2024 at 15:27:21