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
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0911
Laplacian eigenmaps; Incremental learning; Locally linear
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Li, H.S.[Hou-Sen],
Jiang, H.[Hao],
Barrio, R.[Roberto],
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Cheng, L.Z.[Li-Zhi],
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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
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Wang, Y.[Yong],
Incremental learning from chunk data for IDR/QR,
IVC(36), No. 1, 2015, pp. 1-8.
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1504
Feature extraction
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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
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Zhang, Z.,
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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
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Li, Y.C.[Yan-Chao],
Wang, Y.L.[Yong-Li],
Liu, Q.[Qi],
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Jiang, X.H.[Xiao-Hui],
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Incremental semi-supervised learning on streaming data,
PR(88), 2019, pp. 383-396.
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1901
Semi-supervised learning, Dynamic feature learning,
Streaming data, Classification
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Besedin, A.[Andrey],
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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
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Peng, C.[Can],
Zhao, K.[Kun],
Lovell, B.C.[Brian C.],
Faster ILOD: Incremental learning for object detectors based on
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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
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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
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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
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Liu, Y.Y.[Yu-Yang],
Cong, Y.[Yang],
Sun, G.[Gan],
Ding, Z.M.[Zheng-Ming],
Lifelong Visual-Tactile Spectral Clustering for Robotic Object
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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
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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
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Wang, X.M.[Xiu-Mei],
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Support structure representation learning for sequential data
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PR(122), 2022, pp. 108326.
Elsevier DOI
2112
Sequential data, Clustering, Support structure representation
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Zhou, S.[Shiji],
Wang, L.[Lianzhe],
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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
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He, C.[Chen],
Wang, R.P.[Rui-Ping],
Chen, X.L.[Xi-Lin],
Rethinking class orders and transferability in class incremental
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PRL(161), 2022, pp. 67-73.
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2209
Transferability, Class incremental learning, Class order
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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
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Yu, H.[Hang],
Liu, W.[Weixu],
Lu, J.[Jie],
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Detecting group concept drift from multiple data streams,
PR(134), 2023, pp. 109113.
Elsevier DOI
2212
Concept drift, Data streams, Online learning, Hypothesis test
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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
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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
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Wang, X.[Xuan],
Ji, Z.[Zhong],
Yu, Y.L.[Yun-Long],
Pang, Y.W.[Yan-Wei],
Han, J.G.[Jun-Gong],
Model Attention Expansion for Few-Shot Class-Incremental Learning,
IP(33), 2024, pp. 4419-4431.
IEEE DOI
2408
Adaptation models, Task analysis, Training, Power capacitors,
Feature extraction, Predictive models, Scattering, attention field
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
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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
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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],
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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
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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
Luo, Y.[Yong],
Ge, H.W.[Hong-Wei],
Liu, Y.X.[Yu-Xuan],
Wu, C.G.[Chun-Guo],
Representation Robustness and Feature Expansion for Exemplar-Free
Class-Incremental Learning,
CirSysVideo(34), No. 7, July 2024, pp. 5306-5320.
IEEE DOI
2407
Task analysis, Prototypes, Thermal stability, Feature extraction, Training,
Adaptation models, Data models, Batch normalization, prototype mixing
BibRef
Pan, Z.C.[Zi-Cheng],
Zhang, W.C.[Wei-Chuan],
Yu, X.H.[Xiao-Han],
Zhang, M.[Miaohua],
Gao, Y.S.[Yong-Sheng],
Pseudo-set Frequency Refinement architecture for fine-grained
few-shot class-incremental learning,
PR(155), 2024, pp. 110686.
Elsevier DOI
2408
Few-shot class-incremental learning,
Fine-grained classification, Frequency analysis, Feature space optimization
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
Shao, M.W.[Ming-Wen],
Zhuang, X.K.[Xin-Kai],
Zhang, L.X.[Li-Xu],
Zuo, W.M.[Wang-Meng],
Pseudo initialization based Few-Shot Class Incremental Learning,
CVIU(247), 2024, pp. 104067.
Elsevier DOI
2408
Few-Shot Class Incremental Learning, Embedding space, Pseudo initialization
BibRef
Zhou, Y.H.[Yu-Hang],
Yao, J.[Jiangchao],
Hong, F.[Feng],
Zhang, Y.[Ya],
Wang, Y.F.[Yan-Feng],
Balanced Destruction-Reconstruction Dynamics for Memory-Replay Class
Incremental Learning,
IP(33), 2024, pp. 4966-4981.
IEEE DOI
2409
Training, Incremental learning, Stability analysis,
Image reconstruction, Benchmark testing, Costs, Thermal stability,
data imbalance
BibRef
Liu, W.Z.[Wen-Zhuo],
Wu, X.J.[Xin-Jian],
Zhu, F.[Fei],
Yu, M.M.[Ming-Ming],
Wang, C.[Chuang],
Liu, C.L.[Cheng-Lin],
Class Incremental Learning with Self-Supervised Pre-Training and
Prototype Learning,
PR(157), 2025, pp. 110943.
Elsevier DOI
2409
Class incremental learning, Catastrophic forgetting,
Prototype learning, Self-supervised 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
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],
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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
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Roy, S.[Soumya],
Verma, V.[Vinay],
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Efficient Expansion and Gradient Based Task Inference for Replay Free
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WACV24(1154-1164)
IEEE DOI
2404
Adaptation models, Transfer learning, Predictive models,
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ICCV23(11802-11812)
IEEE DOI Code:
WWW Link.
2401
BibRef
Hakim, G.A.V.[Gustavo A. Vargas],
Osowiechi, D.[David],
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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
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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 .