14.2.7.1 Dynamic Learning, Incremental 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

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

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

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

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

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

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

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.Z.[Xi-Zhao],
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.Y.[Lian-Yu], 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

Frascaroli, E.[Emanuele], Benaglia, R.[Riccardo], Boschini, M.[Matteo], Moschella, L.[Luca], Fiorini, C.[Cosimo], Rodolà, E.[Emanuele], Calderara, S.[Simone],
Latent spectral regularization for continual learning,
PRL(184), 2024, pp. 119-125.
Elsevier DOI 2408
Continual learning, Deep learning, Regularization, Spectral geometry, Incremental learning BibRef

Taheri, S.[Sona], Bagirov, A.M.[Adil M.], Sultanova, N.[Nargiz], Ordin, B.[Burak],
Robust clustering algorithm: The use of soft trimming approach,
PRL(185), 2024, pp. 15-22.
Elsevier DOI 2410
Partitional clustering, Robust clustering, Incremental clustering, Trimming approach BibRef

Peng, C.[Can], Koniusz, P.[Piotr], Guo, K.Y.[Kai-Yu], Lovell, B.C.[Brian C.], Moghadam, P.[Peyman],
Multivariate prototype representation for domain-generalized incremental learning,
CVIU(249), 2024, pp. 104215.
Elsevier DOI 2412
Incremental learning, Domain generalization BibRef

Mazumder, P.[Pratik], Singh, P.[Pravendra], Rai, P.[Piyush], Namboodiri, V.P.[Vinay P.],
Rectification-Based Knowledge Retention for Task Incremental Learning,
PAMI(46), No. 3, March 2024, pp. 1561-1575.
IEEE DOI 2402
BibRef
Earlier: A2, A1, A3, A4:
Rectification-based Knowledge Retention for Continual Learning,
CVPR21(15277-15286)
IEEE DOI 2111
Task analysis, Training, Testing, Data models, Adaptation models, Training data, Deep learning, Continual learning, deep learning, task incremental learning. Learning systems. BibRef

Liu, Z.C.[Zi-Chen], Peng, Y.X.[Yu-Xin], Zhou, J.H.[Jia-Huan],
Compositional Prompting for Anti-Forgetting in Domain Incremental Learning,
IJCV(132), No. 12, December 2024, pp. 5783-5800.
Springer DOI 2501
BibRef

Shao, Y.Z.[Ying-Zhao], Li, Y.S.[Yun-Song], Han, X.D.[Xiao-Dong],
Contrastive Dual-Pool Feature Adaption for Domain Incremental Remote Sensing Scene Classification,
RS(17), No. 2, 2025, pp. 308.
DOI Link 2502
BibRef

Qian, X.T.[Xiao-Tong], Cabanes, G.[Guénaël], Rastin, P.[Parisa], Guidani, M.A.[Mohamed Alae], Marrakchi, G.[Ghassen], Clausel, M.[Marianne], Grozavu, N.[Nistor],
Incremental clustering based on Wasserstein distance between histogram models,
PR(162), 2025, pp. 111414.
Elsevier DOI 2503
Unsupervised learning, Static and dynamic clustering, Large datasets, Data streams, Sliding windows, Wasserstein distance BibRef

Li, Y.C.[Yi-Chen], Wang, H.Z.[Hao-Zhao], Qi, Y.[Yining], Liu, W.[Wei], Li, R.X.[Rui-Xuan],
Re-Fed+: A Better Replay Strategy for Federated Incremental Learning,
PAMI(47), No. 7, July 2025, pp. 5489-5500.
IEEE DOI 2506
Training, Incremental learning, Data models, Federated learning, Adaptation models, Servers, Pattern analysis, Data privacy, Costs, synergistic replay BibRef

Zhong, J.[Jian], Jiao, Y.F.[Yi-Fan], Bao, B.K.[Bing-Kun],
Replay-Based Incremental Object Detection With Local Response Exploration,
MultMed(27), 2025, pp. 4348-4360.
IEEE DOI 2507
Training, Object detection, Feature extraction, Detectors, Automobiles, Overfitting, Entropy, Incremental learning, Head, deep learning 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 With Self-Supervision for Incremental Learning,
PAMI(47), No. 4, April 2025, pp. 2563-2580.
IEEE DOI 2503
Incremental learning, Vectors, Null space, Covariance matrices, Knowledge engineering, Training, Approximation algorithms, stability-plasticity dilemma BibRef

Sun, Y.F.[Yan-Feng], Zhang, J.X.[Jia-Xing], Zhang, Q.[Qi], Wang, S.[Shaofan], Yin, B.C.[Bao-Cai],
United diverse subgraph for graph incremental learning,
PRL(196), 2025, pp. 206-212.
Elsevier DOI 2509
Graph incremental learning, Graph neural networks, Node pooling BibRef

Lee, Y.[Yeseok], Lee, D.[Donghyeon], Kwak, T.[Taehong], Kim, Y.[Yongil],
ER-PASS: Experience Replay with Performance-Aware Submodular Sampling for Domain-Incremental Learning in Remote Sensing,
RS(17), No. 18, 2025, pp. 3233.
DOI Link 2510
BibRef

Qiao, J.Y.[Jing-Yang], Zhang, Z.Z.[Zhi-Zhong], Tan, X.[Xin], Qu, Y.Y.[Yan-Yun], Zhang, W.[Wensheng], Han, Z.[Zhi], Xie, Y.[Yuan],
Gradient Projection for Continual Parameter-Efficient Tuning,
PAMI(47), No. 10, October 2025, pp. 9316-9329.
IEEE DOI 2510
Tuning, Training, Adaptation models, Incremental learning, Artificial intelligence, Pattern analysis, multi-modality learning BibRef

Zhang, P.[Peng], Yin, H.P.[Hong-Peng], Zhou, H.[Han],
One-pass online learning from data streams with unpredictable feature evolution,
PR(171), 2026, pp. 112003.
Elsevier DOI 2510
Unpredictable feature evolution, One-pass learning, Kernel-based online learning, Support vector selection strategy BibRef

Feng, Q.[Qian], Zhao, H.[Hanbin], Zhang, C.[Chao], Dong, J.H.[Jia-Hua], Ding, H.H.[Heng-Hui], Jiang, Y.G.[Yu-Gang], Qian, H.[Hui],
PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer,
CirSysVideo(35), No. 11, November 2025, pp. 11282-11296.
IEEE DOI Code:
WWW Link. 2511
Incremental learning, Training, Costs, Privacy, Memory management, Faces, Streaming media, Smart phones, pre-trained model BibRef

Tang, X.J.[Xi-Jia], Xu, C.[Chao], Hou, C.P.[Chen-Ping],
Model Rectification With Simultaneous Incremental Feature and Partial Label Set,
PAMI(47), No. 12, December 2025, pp. 11674-11691.
IEEE DOI 2511
Adaptation models, Classification algorithms, Training, Data models, Phase locked loops, Heuristic algorithms, Accuracy, open and dynamic environment BibRef

Jiang, S.Q.[Sheng-Qin], Fang, Y.Y.[Yao-Yu], Zhang, H.K.[Hao-Kui], Liu, Q.S.[Qing-Shan], Qi, Y.K.[Yuan-Kai], Yang, Y.[Yang], Wang, P.[Peng],
Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-Based Video Incremental Learning,
IJCV(134), No. 4, April 2026, pp. 190.
Springer DOI 2603
BibRef

Qu, Q.[Qian], Wan, X.H.[Xin-Hang], Liu, J.Y.[Ji-Yuan], Liu, X.W.[Xin-Wang], Zhu, E.[En],
Anchor-Guided Sample-and-Feature Incremental Alignment Framework for Multi-View Clustering,
CirSysVideo(36), No. 3, March 2026, pp. 2882-2893.
IEEE DOI 2603
Representation learning, Protection, Noise measurement, Incremental learning, Data privacy, Videos, Training, Optimization, anchor strategy BibRef

Li, Y.C.[Yan-Chao], Dou, H.W.[Hong-Wei], Li, G.X.[Guan-Xiao], Gao, G.W.[Guang-Wei], Zhou, H.Y.[Hui-Yu],
INSERTION: From traditional incremental learning to open-world stream learning,
PR(176), 2026, pp. 113163.
Elsevier DOI 2603
Open-world stream learning, Unseen classes, Incremental learning, Semi-supervised learning, Robustness BibRef

Song, X.[Xiang], He, Y.H.[Yu-Hang], Peng, L.[Lin], Gong, Y.H.[Yi-Hong],
Multi-Task Unified Domain Incremental Learning With Domain Difference Adapters,
IP(35), 2026, pp. 3893-3908.
IEEE DOI 2604
LoRa, Storage area networks, Protocols, Computer networks, Video equipment, Videos, Optical projectors, image classification BibRef

Zhao, D.X.[Dong-Xing], Liu, H.[Hui], Huang, K.[Keju], Yang, J.[Junan], Sun, J.X.[Jia-Xing],
Semi-Supervised Cross-Domain Incremental Learning for Specific Emitter Identification,
SPLetters(33), 2026, pp. 1491-1495.
IEEE DOI 2604
Adaptation models, Entropy, Data models, Training, Fingerprint recognition, Incremental learning, signal processing BibRef

Cao, Z.S.[Zong-Sheng], Xu, Q.Q.[Qian-Qian], Yang, Z.Y.[Zhi-Yong], Cao, X.C.[Xiao-Chun], Huang, Q.M.[Qing-Ming],
CAKGE: Context-Aware Adaptive Learning for Dynamic Knowledge Graph Embeddings,
PAMI(48), No. 6, June 2026, pp. 6225-6240.
IEEE DOI 2605
Knowledge graphs, Adaptation models, Semantics, Cognition, Context modeling, Adaptive learning, Training, Accuracy, context-aware learning BibRef

Yin, H.W.[Hong-Wei], Chen, H.P.[Hong-Peng], Hu, W.J.[Wen-Jun], Zhou, P.[Peng], Wang, S.T.[Shi-Tong],
Contrast-driven incremental multi-view clustering with semantic distillation and adaptive graph fusion,
PR(179), 2026, pp. 113856.
Elsevier DOI Code:
WWW Link. 2606
Multi-view clustering, Incremental learning, Semantic distillation, Graph fusion, Knowledge transfer BibRef

Xu, C.C.[Cheng-Cheng], Zhao, H.Y.[Hai-Yan], Lu, X.H.[Xing-Hao], Gao, B.Z.[Bing-Zhao], Chen, H.[Hong],
RD-IARL: Incremental action reinforcement learning based on reward deviation for multi-view end-to-end autonomous driving,
PR(180), 2026, pp. 114361.
Elsevier DOI Code:
WWW Link. 2609
End-to-end, Multi-view, Autonomous driving, Reinforcement learning BibRef

Ta, H.B.[Huu Binh], Nguyen, D.[Duc], Tran, Q.[Quyen], Tran, T.[Toan], Pham, T.[Tung],
Low-Rank Adaptation in Multilinear Operator Networks for Security-Preserving Incremental Learning,
CVPR25(24341-24350)
IEEE DOI 2508
Adaptation models, Incremental learning, Tensors, Computational modeling, Vectors, Polynomials, Proposals, polynomial network BibRef

Hegde, N.[Niharika], Muralidhara, S.[Shishir], Schuster, R.[René], Stricker, D.[Didier],
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-Based Semantic Segmentation,
WACV25(5540-5549)
IEEE DOI 2505
Visualization, Incremental learning, Semantic segmentation, Scalability, Prevention and mitigation, continual semantic segmentation BibRef

Leotescu, G.[George], Popa, A.I.[Alin-Ionut], Grigore, D.[Diana], Voinea, D.[Daniel], Perona, P.[Pietro],
Self-Supervised Incremental Learning of Object Representations from Arbitrary Image Sets,
WACV25(8144-8154)
IEEE DOI Code:
WWW Link. 2505
Training, Visualization, Incremental learning, Codes, Noise, Image retrieval, Object segmentation, Logic gates, Resilience, unsupervised object segmentation BibRef

Brachmann, E.[Eric], Wynn, J.[Jamie], Chen, S.[Shuai], Cavallari, T.[Tommaso], Monszpart, Á.[Áron], Turmukhambetov, D.[Daniyar], Prisacariu, V.A.[Victor Adrian],
Scene Coordinate Reconstruction: Posing of Image Collections via Incremental Learning of a Relocalizer,
ECCV24(LVI: 421-440).
Springer DOI 2412
BibRef

Wang, Q.[Qiang], He, Y.H.[Yu-Hang], Dong, S.L.[Song-Lin], Gao, X.Y.[Xin-Yuan], Wang, S.K.[Shao-Kun], Gong, Y.H.[Yi-Hong],
Non-exemplar Domain Incremental Learning via Cross-domain Concept Integration,
ECCV24(XLIX: 144-162).
Springer DOI 2412
BibRef

Li, Y.C.[Yi-Chen], Xu, W.C.[Wen-Chao], Wang, H.Z.[Hao-Zhao], Qi, Y.N.[Yi-Ning], Guo, J.C.[Jing-Cai], Li, R.X.[Rui-Xuan],
Personalized Federated Domain-incremental Learning Based on Adaptive Knowledge Matching,
ECCV24(XLVI: 127-144).
Springer DOI 2412
BibRef

Qin, S.Y.[Shi-Yu], Zhou, Y.M.[Yi-Min], Wang, J.P.[Jin-Peng], Chen, B.[Bin], An, B.Y.[Bao-Yi], Dai, T.[Tao], Xia, S.T.[Shu-Tao],
Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression,
ICIP24(1767-1773)
IEEE DOI 2411
Visualization, Image coding, Bit rate, Rate-distortion, Transformers, Feature extraction, Decoding, image compression, variable rate, xvisual prompt tuning BibRef

Fei, X.[Xiang], Zheng, X.[Xiawu], Wang, Y.[Yan], Chao, F.[Fei], Wu, C.L.[Cheng-Lin], Cao, L.J.[Liu-Juan],
RepAn: Enhanced Annealing through Re-parameterization,
CVPR24(5798-5808)
IEEE DOI Code:
WWW Link. 2410
Training, Annealing, Incremental learning, Costs, Simulated annealing, Inference algorithms BibRef

Tan, Y.[Yuwen], Zhou, Q.[Qinhao], Xiang, X.[Xiang], Wang, K.[Ke], Wu, Y.C.[Yu-Chuan], Li, Y.B.[Yong-Bin],
Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer,
CVPR24(23252-23262)
IEEE DOI 2410
Adaptation models, Semantics, Prototypes, Benchmark testing, Incremental Learning, Adapter Tuning BibRef

Kamath, S.[Sandesh], Soutif-Cormerais, A.[Albin], van de Weijer, J.[Joost], Raducanu, B.[Bogdan],
The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks,
CLVision24(4182-4186)
IEEE DOI 2410
Training, Incremental learning, Employment, Stability analysis, Energy efficiency, Trajectory, Stability Gap 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

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

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

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

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

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

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

Pégeot, T.[Tom], Feillet, E.[Eva], Popescu, A.[Adrian], Kucher, I.[Inna], Delezoide, B.[Bertrand],
Temporal Dynamics in Visual Data: Analyzing the Impact of Time on Classification Accuracy,
WACV25(6932-6943)
IEEE DOI 2505
Training, Visualization, Adaptation models, Accuracy, Animals, Computational modeling, Web sites, Multimedia communication, domain-incremental learning 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, Inference algorithms, 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

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

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

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

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 BibRef

Sain, A.[Aneeshan], Maity, S.[Subhajit], Chowdhury, P.N.[Pinaki Nath], Koley, S.[Shubhadeep], Bhunia, A.K.[Ayan Kumar], Song, Y.Z.[Yi-Zhe],
Sketch Down the FLOPs: Towards Efficient Networks for Human Sketch,
CVPR25(28383-28393)
IEEE DOI 2508
Knowledge engineering, Adaptation models, Image recognition, Accuracy, Computational modeling, Image retrieval, knowledge distillation 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

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

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, 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

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, 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, Computational efficiency, Task analysis BibRef

Sun, W.J.[Wen-Ju], Zhang, J.[Jing], Wang, D.Y.[Dan-Yu], Geng, Y.L.A.[Yang-Li-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, 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 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

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

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

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

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

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
Class Incremental Learning .