Hong, Y.[Yi],
Kwong, S.[Sam],
Chang, Y.C.[Yu-Chou],
Ren, Q.S.[Qing-Sheng],
Unsupervised feature selection using clustering ensembles and
population based incremental learning algorithm,
PR(41), No. 9, September 2008, pp. 2742-2756.
Elsevier DOI
0806
Clustering ensembles; Dimensionality unbiased;
Population based incremental learning algorithm;
Unsupervised feature selection
BibRef
Lughofer, E.[Edwin],
Extensions of vector quantization for incremental clustering,
PR(41), No. 3, March 2008, pp. 995-1011.
Elsevier DOI
0711
Vector quantization; Clustering; Incremental learning;
New winning cluster selection strategy; Removing cluster satellites;
Split-and-merge strategy; Image classification framework; Fault detection;
Evolving fuzzy models
BibRef
Jia, P.[Peng],
Yin, J.S.[Jun-Song],
Huang, X.S.[Xin-Sheng],
Hu, D.[Dewen],
Incremental Laplacian eigenmaps by preserving adjacent information
between data points,
PRL(30), No. 16, 1 December 2009, pp. 1457-1463.
Elsevier DOI
0911
Laplacian eigenmaps; Incremental learning; Locally linear
construction; Nonlinear dimensionality reduction
BibRef
Li, H.S.[Hou-Sen],
Jiang, H.[Hao],
Barrio, R.[Roberto],
Liao, X.K.[Xiang-Ke],
Cheng, L.Z.[Li-Zhi],
Su, F.[Fang],
Incremental manifold learning by spectral embedding methods,
PRL(32), No. 10, 15 July 2011, pp. 1447-1455.
Elsevier DOI
1106
Manifold learning; Incremental learning; Dimensionality reduction;
Spectral embedding methods; Hessian eigenmaps
BibRef
Lu, G.F.[Gui-Fu],
Jian, Z.[Zou],
Wang, Y.[Yong],
Incremental learning from chunk data for IDR/QR,
IVC(36), No. 1, 2015, pp. 1-8.
Elsevier DOI
1504
Feature extraction
incremental dimension reduction.
BibRef
Le, T.B.[Thanh-Binh],
Kim, S.W.[Sang-Woon],
On incrementally using a small portion of strong unlabeled data for
semi-supervised learning algorithms,
PRL(41), No. 1, 2014, pp. 53-64.
Elsevier DOI
1403
Semi-supervised learning
BibRef
Zhang, Z.,
Li, Y.,
Zhang, Z.,
Jin, C.,
Gao, M.,
Adaptive Matrix Sketching and Clustering for Semisupervised
Incremental Learning,
SPLetters(25), No. 7, July 2018, pp. 1069-1073.
IEEE DOI
1807
learning (artificial intelligence), matrix algebra,
pattern classification, adaptive matrix sketching,
semisupervised classification
BibRef
Li, Y.C.[Yan-Chao],
Wang, Y.L.[Yong-Li],
Liu, Q.[Qi],
Bi, C.[Cheng],
Jiang, X.H.[Xiao-Hui],
Sun, S.R.[Shu-Rong],
Incremental semi-supervised learning on streaming data,
PR(88), 2019, pp. 383-396.
Elsevier DOI
1901
Semi-supervised learning, Dynamic feature learning,
Streaming data, Classification
BibRef
Besedin, A.[Andrey],
Blanchart, P.[Pierre],
Crucianu, M.[Michel],
Ferecatu, M.[Marin],
Deep online classification using pseudo-generative models,
CVIU(201), 2020, pp. 103048.
Elsevier DOI
2011
Avoid issues of forgetting.
Deep learning, Online learning, Pseudo-generative models, Stream learning
BibRef
Peng, C.[Can],
Zhao, K.[Kun],
Lovell, B.C.[Brian C.],
Faster ILOD: Incremental learning for object detectors based on
faster RCNN,
PRL(140), 2020, pp. 109-115.
Elsevier DOI
2012
Deep learning, Object detection, Incremental learning
BibRef
Wang, G.X.[Guang-Xing],
Ren, P.[Peng],
Hyperspectral Image Classification with Feature-Oriented Adversarial
Active Learning,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link
2012
BibRef
Xiang, S.C.[Sun-Cheng],
Fu, Y.Z.[Yu-Zhuo],
Liu, T.[Ting],
Progressive learning with style transfer for distant domain adaptation,
IET-IPR(14), No. 14, December 2020, pp. 3527-3535.
DOI Link
2012
BibRef
Berger, K.[Katja],
Caicedo, J.P.R.[Juan Pablo Rivera],
Martino, L.[Luca],
Wocher, M.[Matthias],
Hank, T.[Tobias],
Verrelst, J.[Jochem],
A Survey of Active Learning for Quantifying Vegetation Traits from
Terrestrial Earth Observation Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link
2101
Survey, Active Learning.
BibRef
Li, J.[Jia],
Song, Y.F.[Ya-Fei],
Zhu, J.F.[Jian-Feng],
Cheng, L.L.[Le-Le],
Su, Y.[Ying],
Ye, L.[Lin],
Yuan, P.C.[Peng-Cheng],
Han, S.M.[Shu-Min],
Learning From Large-Scale Noisy Web Data With Ubiquitous Reweighting
for Image Classification,
PAMI(43), No. 5, May 2021, pp. 1808-1814.
IEEE DOI
2104
Noise measurement, Deep learning, Task analysis, Training,
Annotations, Solid modeling, Visualization, Image classification,
deep learning
BibRef
Gweon, H.[Hyukjun],
Yu, H.[Hao],
A nearest neighbor-based active learning method and its application
to time series classification,
PRL(146), 2021, pp. 230-236.
Elsevier DOI
2105
Active learning, Batch mode, Time series classification, Nearest neighbor
BibRef
Wang, Y.[Yi],
Ding, Y.[Yi],
He, X.J.[Xiang-Jian],
Fan, X.[Xin],
Lin, C.[Chi],
Li, F.Q.[Feng-Qi],
Wang, T.Z.[Tian-Zhu],
Luo, Z.X.[Zhong-Xuan],
Luo, J.B.[Jie-Bo],
Novelty Detection and Online Learning for Chunk Data Streams,
PAMI(43), No. 7, July 2021, pp. 2400-2412.
IEEE DOI
2106
Kernel, Data models, Linear systems, Fans, Hilbert space,
Streaming media, Feature extraction, Data stream,
online learning
BibRef
Fonseca, J.[Joao],
Douzas, G.[Georgios],
Bacao, F.[Fernando],
Increasing the Effectiveness of Active Learning: Introducing
Artificial Data Generation in Active Learning for Land Use/Land Cover
Classification,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Celik, B.[Bilge],
Vanschoren, J.[Joaquin],
Adaptation Strategies for Automated Machine Learning on Evolving Data,
PAMI(43), No. 9, September 2021, pp. 3067-3078.
IEEE DOI
2108
Pipelines, Adaptation models, Machine learning, Optimization,
Data models, Task analysis, Bayes methods, AutoML, data streams,
adaptation strategies
BibRef
Zheng, X.[Xiawu],
Zhang, Y.[Yang],
Hong, S.[Sirui],
Li, H.X.[Hui-Xia],
Tang, L.[Lang],
Xiong, Y.[Youcheng],
Zhou, J.[Jin],
Wang, Y.[Yan],
Sun, X.S.[Xiao-Shuai],
Zhu, P.F.[Peng-Fei],
Wu, C.[Chenglin],
Ji, R.R.[Rong-Rong],
Evolving Fully Automated Machine Learning via Life-Long Knowledge
Anchors,
PAMI(43), No. 9, September 2021, pp. 3091-3107.
IEEE DOI
2108
Pipelines, Task analysis, Optimization, Data models,
Computational modeling, Training, Search problems,
evolutionary algorithm
BibRef
Wang, K.[Kai],
van de Weijer, J.[Joost],
Herranz, L.[Luis],
ACAE-REMIND for online continual learning with compressed feature
replay,
PRL(150), 2021, pp. 122-129.
Elsevier DOI
2109
online continual learning, autoencoders, vector quantization
BibRef
Agarwal, M.[Mridul],
Aggarwal, V.[Vaneet],
Blind decision making: Reinforcement learning with delayed
observations,
PRL(150), 2021, pp. 176-182.
Elsevier DOI
2109
BibRef
Grigoletto, R.[Riccardo],
Maiettini, E.[Elisa],
Natale, L.[Lorenzo],
Score to Learn: A Comparative Analysis of Scoring Functions for Active
Learning in Robotics,
CVS21(55-67).
Springer DOI
2109
BibRef
Dong, J.H.[Jia-Hua],
Cong, Y.[Yang],
Sun, G.[Gan],
Zhang, T.[Tao],
Lifelong robotic visual-tactile perception learning,
PR(121), 2022, pp. 108176.
Elsevier DOI
2109
Lifelong machine learning, Robotics, Visual-tactile perception,
Cross-modality learning, Multi-task learning
BibRef
Liu, Y.Y.[Yu-Yang],
Cong, Y.[Yang],
Sun, G.[Gan],
Ding, Z.M.[Zheng-Ming],
Lifelong Visual-Tactile Spectral Clustering for Robotic Object
Perception,
CirSysVideo(33), No. 2, February 2023, pp. 818-829.
IEEE DOI
2302
Task analysis, Robots, Libraries, Manifolds, Correlation,
Computational modeling, Visualization, Lifelong learning,
modality-consistent and modality-invariant
BibRef
Huang, F.X.[Fu-Xian],
Li, W.C.[Wei-Chao],
Cui, J.B.[Jia-Bao],
Fu, Y.J.[Yong-Jian],
Li, X.[Xi],
Unified curiosity-Driven learning with smoothed intrinsic reward
estimation,
PR(123), 2022, pp. 108352.
Elsevier DOI
2112
Reinforcement learning, Unified curiosity-driven exploration,
Robust intrinsic reward, Task-relevant feature
BibRef
Yang, Y.[Yang],
Chen, B.[Bo],
Liu, H.W.[Hong-Wei],
Bayesian compression for dynamically expandable networks,
PR(122), 2022, pp. 108260.
Elsevier DOI
2112
Bayesian compression, DEN, Continual learning,
Selective retraining, Dynamically expands network, Semantic drift
BibRef
Wang, X.M.[Xiu-Mei],
Guo, D.N.[Ding-Ning],
Cheng, P.T.[Pei-Tao],
Support structure representation learning for sequential data
clustering,
PR(122), 2022, pp. 108326.
Elsevier DOI
2112
Sequential data, Clustering, Support structure representation
BibRef
Li, C.S.[Chang-Sheng],
Li, R.Q.[Rong-Qing],
Yuan, Y.[Ye],
Wang, G.R.[Guo-Ren],
Xu, D.[Dong],
Deep Unsupervised Active Learning via Matrix Sketching,
IP(30), 2021, pp. 9280-9293.
IEEE DOI
2112
Image reconstruction, Image processing, Data models, Task analysis,
Learning systems, Kernel, Manifolds, Unsupervised active learning,
data reconstruction
BibRef
Lomonaco, V.[Vincenzo],
Pellegrini, L.[Lorenzo],
Rodriguez, P.[Pau],
Caccia, M.[Massimo],
She, Q.[Qi],
Chen, Y.[Yu],
Jodelet, Q.[Quentin],
Wang, R.P.[Rui-Ping],
Mai, Z.[Zheda],
Vazquez, D.[David],
Parisi, G.I.[German I.],
Churamani, N.[Nikhil],
Pickett, M.[Marc],
Laradji, I.[Issam],
Maltoni, D.[Davide],
CVPR 2020 continual learning in computer vision competition:
Approaches, results, current challenges and future directions,
AI(303), 2022, pp. 103635.
Elsevier DOI
2201
Continual learning, Lifelong learning, Incremental learning, Challenge
BibRef
Zhou, S.[Shiji],
Wang, L.[Lianzhe],
Zhang, S.H.[Shang-Hang],
Wang, Z.[Zhi],
Zhu, W.[Wenwu],
Active Gradual Domain Adaptation: Dataset and Approach,
MultMed(24), 2022, pp. 1210-1220.
IEEE DOI
2203
Adaptation models, Uncertainty, Data models, Diversity reception,
Deep learning, Performance evaluation, Internet, web noise data
BibRef
Wu, D.R.[Dong-Rui],
Huang, J.[Jian],
Affect Estimation in 3D Space Using Multi-Task Active Learning for
Regression,
AffCom(13), No. 1, January 2022, pp. 16-27.
IEEE DOI
2203
Task analysis, Affective computing, Estimation, Labeling,
Computational modeling, Training, Active learning,
greedy sampling
BibRef
Li, C.S.[Chang-Sheng],
Ma, H.D.[Han-Dong],
Yuan, Y.[Ye],
Wang, G.R.[Guo-Ren],
Xu, D.[Dong],
Structure Guided Deep Neural Network for Unsupervised Active Learning,
IP(31), No. 2022, pp. 2767-2781.
IEEE DOI
2204
Data models, Kernel, Task analysis, Image reconstruction, Training,
Manifolds, Deep learning, Unsupervised active learning,
imbalance data
BibRef
Wang, J.[Jiao],
Zhang, L.[Lemin],
He, Z.Q.[Zhi-Qiang],
Zhu, C.[Can],
Zhao, Z.[Zihui],
Erlang planning network: An iterative model-based reinforcement
learning with multi-perspective,
PR(128), 2022, pp. 108668.
Elsevier DOI
2205
Model-based reinforcement learning, Multi-perspective,
Bi-level, Planning, Trajectory imagination
BibRef
Korycki, L.[Lukasz],
Krawczyk, B.[Bartosz],
Instance exploitation for learning temporary concepts from sparsely
labeled drifting data streams,
PR(129), 2022, pp. 108749.
Elsevier DOI
2206
BibRef
Earlier:
Class-Incremental Experience Replay for Continual Learning under
Concept Drift,
OmniCV21(3644-3653)
IEEE DOI
2109
Machine learning, Data stream mining, Concept drift,
Sparse labeling, Active learning.
Machine learning, Data mining, Task analysis
BibRef
de Lange, M.[Matthias],
Aljundi, R.[Rahaf],
Masana, M.[Marc],
Parisot, S.[Sarah],
Jia, X.[Xu],
Leonardis, A.[Aleš],
Slabaugh, G.[Gregory],
Tuytelaars, T.[Tinne],
A Continual Learning Survey: Defying Forgetting in Classification
Tasks,
PAMI(44), No. 7, July 2022, pp. 3366-3385.
IEEE DOI
2206
Survey, Continual Learning. Task analysis, Knowledge engineering, Neural networks, Training,
Training data, Learning systems, Interference, Continual learning,
neural networks
BibRef
Shen, Y.[Yeji],
Song, Y.H.[Yu-Hang],
Wu, C.H.[Chi-Hao],
Kuo, C.C.J.[C.C. Jay],
TBAL: Two-stage batch-mode active learning for image classification,
SP:IC(106), 2022, pp. 116731.
Elsevier DOI
2206
Active learning, Image classification, Semi-supervised learning
BibRef
Lan, C.L.[Chuan-Lin],
Feng, F.[Fan],
Liu, Q.[Qi],
She, Q.[Qi],
Yang, Q.[Qihan],
Hao, X.Y.[Xin-Yue],
Mashkin, I.[Ivan],
Kei, K.S.[Ka Shun],
Qiang, D.[Dong],
Lomonaco, V.[Vincenzo],
Shi, X.S.[Xue-Song],
Wang, Z.W.[Zheng-Wei],
Guo, Y.[Yao],
Zhang, Y.M.[Yi-Min],
Qiao, F.[Fei],
Chan, R.H.M.[Rosa H.M.],
Towards lifelong object recognition: A dataset and benchmark,
PR(130), 2022, pp. 108819.
Elsevier DOI
2206
Robotic vision, Continual learning, Lifelong learning, Object recognition
BibRef
Yang, Y.Z.[Ya-Zhou],
Loog, M.[Marco],
To Actively Initialize Active Learning,
PR(131), 2022, pp. 108836.
Elsevier DOI
2208
active learning, active initialization,
nearest neighbor criterion, minimum nearest neighbor distance
BibRef
Koçanaogullari, A.[Aziz],
Akcakaya, M.[Murat],
Erdogmus, D.[Deniz],
Stopping Criterion Design for Recursive Bayesian Classification:
Analysis and Decision Geometry,
PAMI(44), No. 9, September 2022, pp. 5590-5601.
IEEE DOI
2208
Uncertainty, Entropy, Bayes methods, Geometry, Radar tracking,
Probability distribution, Brain modeling, Active learning,
optimal stopping criterion design
BibRef
Li, M.[Min],
Huang, T.Y.[Tian-Yi],
Zhu, W.[William],
Clustering experience replay for the effective exploitation in
reinforcement learning,
PR(131), 2022, pp. 108875.
Elsevier DOI
2208
Reinforcement learning, Clustering, Experience replay,
Exploitation efficiency, Time division
BibRef
He, C.[Chen],
Wang, R.P.[Rui-Ping],
Chen, X.L.[Xi-Lin],
Rethinking class orders and transferability in class incremental
learning,
PRL(161), 2022, pp. 67-73.
Elsevier DOI
2209
Transferability, Class incremental learning, Class order
BibRef
Tosatto, S.[Samuele],
Carvalho, J.[João],
Peters, J.[Jan],
Batch Reinforcement Learning With a Nonparametric Off-Policy Policy
Gradient,
PAMI(44), No. 10, October 2022, pp. 5996-6010.
IEEE DOI
2209
Mathematical model, Estimation, Kernel, Reinforcement learning,
Monte Carlo methods, Task analysis, Closed-form solutions,
nonparametric estimation
BibRef
Akrour, R.[Riad],
Tateo, D.[Davide],
Peters, J.[Jan],
Continuous Action Reinforcement Learning From a Mixture of
Interpretable Experts,
PAMI(44), No. 10, October 2022, pp. 6795-6806.
IEEE DOI
2209
Task analysis, Complexity theory, Approximation algorithms,
Neural networks, Trajectory, Reinforcement learning,
robotics
BibRef
Xu, J.[Ju],
Ma, J.[Jin],
Gao, X.S.[Xue-Song],
Zhu, Z.X.[Zhan-Xing],
Adaptive Progressive Continual Learning,
PAMI(44), No. 10, October 2022, pp. 6715-6728.
IEEE DOI
2209
Task analysis, Optimization, Bayes methods, Training,
Reinforcement learning, Knowledge engineering, Complexity theory,
neural networks
BibRef
Zhuang, C.[Chen],
Huang, S.L.[Shao-Li],
Cheng, G.[Gong],
Ning, J.F.[Ji-Feng],
Multi-criteria Selection of Rehearsal Samples for Continual Learning,
PR(132), 2022, pp. 108907.
Elsevier DOI
2209
Continual Learning, Multiple Criteria, Rehersal Method, Learning to learn
BibRef
Wan, Y.Y.[Yuan-Yu],
Zhang, L.J.[Li-Jun],
Efficient Adaptive Online Learning via Frequent Directions,
PAMI(44), No. 10, October 2022, pp. 6910-6923.
IEEE DOI
2209
Complexity theory, Time complexity, Optimization, Mirrors,
Approximation algorithms, Symmetric matrices, Transforms,
adaptive subgradient methods
BibRef
Zhang, J.[Ji],
Song, J.K.[Jing-Kuan],
Gao, L.[Lianli],
Liu, Y.[Ye],
Shen, H.T.[Heng Tao],
Progressive Meta-Learning With Curriculum,
CirSysVideo(32), No. 9, September 2022, pp. 5916-5930.
IEEE DOI
2209
Task analysis, Training, Adaptation models, Computational modeling,
Ear, Standards, Pediatrics, Few-shot learning, meta-learning,
hard task-sampling
BibRef
Zhao, X.Y.[Xing-Yu],
An, Y.X.[Yue-Xuan],
Xu, N.[Ning],
Geng, X.[Xin],
Continuous label distribution learning,
PR(133), 2023, pp. 109056.
Elsevier DOI
2210
Label distribution learning, Continuous label distribution,
Label ambiguity, Label encoding, Label correlations
BibRef
Zhang, M.Y.[Meng-Yang],
Tian, G.H.[Guo-Hui],
Gao, H.B.[Huan-Bing],
Zhang, Y.[Ying],
Autonomous Generation of Service Strategy for Household Tasks:
A Progressive Learning Method With A Priori Knowledge and Reinforcement
Learning,
CirSysVideo(32), No. 11, November 2022, pp. 7473-7488.
IEEE DOI
2211
Correlation, Task analysis, Reinforcement learning,
Artificial neural networks, reinforcement learning
BibRef
Chen, X.[Xu],
Wujek, B.[Brett],
A Unified Framework for Automatic Distributed Active Learning,
PAMI(44), No. 12, December 2022, pp. 9774-9786.
IEEE DOI
2212
Optimization, Semisupervised learning, Machine learning,
Distributed databases, Big Data, Search problems,
active learning
BibRef
Jodelet, Q.[Quentin],
Liu, X.[Xin],
Murata, T.[Tsuyoshi],
Balanced softmax cross-entropy for incremental learning with and
without memory,
CVIU(225), 2022, pp. 103582.
Elsevier DOI
2212
Continual learning, Class incremental learning,
Image classification, Bias mitigation
BibRef
Yu, H.[Hang],
Liu, W.[Weixu],
Lu, J.[Jie],
Wen, Y.M.[Yi-Min],
Luo, X.F.[Xiang-Feng],
Zhang, G.Q.[Guang-Quan],
Detecting group concept drift from multiple data streams,
PR(134), 2023, pp. 109113.
Elsevier DOI
2212
Concept drift, Data streams, Online learning, Hypothesis test
BibRef
Thuseethan, S.[Selvarajah],
Rajasegarar, S.[Sutharshan],
Yearwood, J.[John],
Deep Continual Learning for Emerging Emotion Recognition,
MultMed(24), 2022, pp. 4367-4380.
IEEE DOI
2212
Emotion recognition, Task analysis, Feature extraction,
Learning systems, Transfer learning, Training, Databases,
unknown emotions
BibRef
Yang, H.Z.[Hong-Zheng],
Chen, C.[Cheng],
Jiang, M.[Meirui],
Liu, Q.[Quande],
Cao, J.F.[Jian-Feng],
Heng, P.A.[Pheng Ann],
Dou, Q.[Qi],
DLTTA: Dynamic Learning Rate for Test-Time Adaptation on Cross-Domain
Medical Images,
MedImg(41), No. 12, December 2022, pp. 3575-3586.
IEEE DOI
2212
Adaptation models, Data models, Training, Predictive models,
Training data, Computational modeling, Task analysis, dynamic learning rate
BibRef
Hu, Q.H.[Qing-Hua],
Gao, Y.C.[Yu-Cong],
Cao, B.[Bing],
Curiosity-Driven Class-Incremental Learning via Adaptive Sample
Selection,
CirSysVideo(32), No. 12, December 2022, pp. 8660-8673.
IEEE DOI
2212
Task analysis, Adaptation models, Knowledge engineering,
Data models, Uncertainty, Training, Computational modeling, novelty
BibRef
Ji, Z.[Zhong],
Li, J.[Jin],
Wang, Q.[Qiang],
Zhang, Z.F.[Zhong-Fei],
Complementary Calibration: Boosting General Continual Learning With
Collaborative Distillation and Self-Supervision,
IP(32), 2023, pp. 657-667.
IEEE DOI
2301
Task analysis, Feature extraction, Calibration, Collaboration,
Training, Testing, Ear, General continual learning,
supervised contrastive learning
BibRef
Ji, Z.[Zhong],
Hou, Z.[Zhishen],
Liu, X.[Xiyao],
Pang, Y.W.[Yan-Wei],
Li, X.L.[Xue-Long],
Memorizing Complementation Network for Few-Shot Class-Incremental
Learning,
IP(32), 2023, pp. 937-948.
IEEE DOI
2301
Task analysis, Power capacitors, Ensemble learning, Knowledge engineering,
Feature extraction, Adaptation models, memorizing complementation
BibRef
Yang, B.[Boyu],
Lin, M.B.[Ming-Bao],
Zhang, Y.X.[Yun-Xiao],
Liu, B.H.[Bing-Hao],
Liang, X.D.[Xiao-Dan],
Ji, R.R.[Rong-Rong],
Ye, Q.X.[Qi-Xiang],
Dynamic Support Network for Few-Shot Class Incremental Learning,
PAMI(45), No. 3, March 2023, pp. 2945-2951.
IEEE DOI
2302
Power capacitors, Training, Feature extraction, Adaptation models,
Task analysis, Generators, Data models, support network
BibRef
Bing, Z.S.[Zhen-Shan],
Lerch, D.[David],
Huang, K.[Kai],
Knoll, A.[Alois],
Meta-Reinforcement Learning in Non-Stationary and Dynamic
Environments,
PAMI(45), No. 3, March 2023, pp. 3476-3491.
IEEE DOI
2302
Task analysis, Training, Robots, Adaptation models, Multitasking,
Inference algorithms, Gaussian mixture model, robotic control
BibRef
Fu, Z.L.[Zhi-Ling],
Wang, Z.[Zhe],
Xu, X.L.[Xin-Lei],
Li, D.D.[Dong-Dong],
Yang, H.[Hai],
Knowledge aggregation networks for class incremental learning,
PR(137), 2023, pp. 109310.
Elsevier DOI
2302
Class incremental learning, Catastrophic forgetting,
Dual-branch network, Knowledge aggregation, Model compression
BibRef
Zamorski, M.[Maciej],
Stypulkowski, M.[Michal],
Karanowski, K.[Konrad],
Trzcinski, T.[Tomasz],
Zieba, M.[Maciej],
Continual learning on 3D point clouds with random compressed
rehearsal,
CVIU(228), 2023, pp. 103621.
Elsevier DOI
2302
Continual learning, Point cloud, Deep learning, Data compression
BibRef
Du, P.[Pan],
Chen, H.[Hui],
Zhao, S.[Suyun],
Chai, S.[Shuwen],
Chen, H.[Hong],
Li, C.P.[Cui-Ping],
Contrastive Active Learning Under Class Distribution Mismatch,
PAMI(45), No. 4, April 2023, pp. 4260-4273.
IEEE DOI
2303
BibRef
Earlier: A1, A3, A2, A4, A5, A6:
Contrastive Coding for Active Learning under Class Distribution
Mismatch,
ICCV21(8907-8916)
IEEE DOI
2203
Semantics, Dogs, Annotations, Data models, Costs, Task analysis,
Supervised learning, Active learning, machine learning.
Costs, Upper bound, Annotations, Text categorization, Representation learning
BibRef
Zhang, X.[Xikun],
Song, D.J.[Dong-Jin],
Tao, D.C.[Da-Cheng],
Hierarchical Prototype Networks for Continual Graph Representation
Learning,
PAMI(45), No. 4, April 2023, pp. 4622-4636.
IEEE DOI
2303
Task analysis, Prototypes, Feature extraction, Memory management,
Knowledge engineering, Representation learning, graph neural networks
BibRef
Martins, V.E.[Vinicius Eiji],
Cano, A.[Alberto],
Barbon Junior, S.[Sylvio],
Meta-learning for dynamic tuning of active learning on stream
classification,
PR(138), 2023, pp. 109359.
Elsevier DOI
2303
Meta-learning, Active learning, Data stream, Concept drift
BibRef
Mahapatra, D.[Dwarikanath],
Poellinger, A.[Alexander],
Reyes, M.[Mauricio],
Graph Node Based Interpretability Guided Sample Selection for Active
Learning,
MedImg(42), No. 3, March 2023, pp. 661-673.
IEEE DOI
2303
Uncertainty, Measurement, Computational modeling, X-ray imaging,
Entropy, Predictive models, Estimation, Interpretability,
sample selection lung disease classification
BibRef
Zhou, S.[Shiji],
Wang, Z.[Zhi],
Hu, C.H.[Cheng-Hao],
Mao, Y.[Yinan],
Yan, H.P.[Hao-Peng],
Zhang, S.H.[Shang-Hang],
Wu, C.[Chuan],
Zhu, W.[Wenwu],
Caching in Dynamic Environments:
A Near-Optimal Online Learning Approach,
MultMed(25), 2023, pp. 792-804.
IEEE DOI
2303
Heuristic algorithms, Streaming media, Size measurement,
Reinforcement learning, Proposals, Optimization, Area measurement,
online learning
BibRef
Boschini, M.[Matteo],
Bonicelli, L.[Lorenzo],
Buzzega, P.[Pietro],
Porrello, A.[Angelo],
Calderara, S.[Simone],
Class-Incremental Continual Learning Into the eXtended DER-Verse,
PAMI(45), No. 5, May 2023, pp. 5497-5512.
IEEE DOI
2304
Task analysis, Training, Proposals, Data models, Optimization,
Knowledge engineering, Interference, Continual learning,
replay methods
BibRef
Hu, H.[Hexiang],
Sener, O.[Ozan],
Sha, F.[Fei],
Koltun, V.[Vladlen],
Drinking From a Firehose: Continual Learning With Web-Scale Natural
Language,
PAMI(45), No. 5, May 2023, pp. 5684-5696.
IEEE DOI
2304
Task analysis, Benchmark testing, Learning systems, Multitasking,
Social networking (online), Neural networks, Data models,
web-scale datasets
BibRef
Guo, J.F.[Ji-Feng],
Pang, Z.Q.[Zhi-Qi],
Bai, M.Y.[Miao-Yuan],
Xiao, Y.B.[Yan-Bang],
Zhang, J.[Jian],
Independency-enhancing adversarial active learning,
IET-IPR(17), No. 5, 2023, pp. 1427-1437.
DOI Link
2304
image classification, image segmentation
BibRef
Lin, H.[Huiwei],
Feng, S.S.[Shan-Shan],
Li, X.[Xutao],
Li, W.T.[Wen-Tao],
Ye, Y.M.[Yun-Ming],
Anchor Assisted Experience Replay for Online Class-Incremental
Learning,
CirSysVideo(33), No. 5, May 2023, pp. 2217-2232.
IEEE DOI
2305
Automobiles, Airplanes, Task analysis, Atmospheric modeling,
Training, Reservoirs, Memory management,
image recognition
BibRef
Kong, Y.J.[Ya-Jing],
Liu, L.[Liu],
Qiao, M.Y.[Mao-Ying],
Wang, Z.[Zhen],
Tao, D.C.[Da-Cheng],
Trust-Region Adaptive Frequency for Online Continual Learning,
IJCV(131), No. 7, July 2023, pp. 1825-1839.
Springer DOI
2307
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
Baik, J.S.[Jae Soon],
Yoon, I.Y.[In Young],
Choi, J.W.[Jun Won],
ST-Conal: Consistency-based Acquisition Criterion Using Temporal
Self-ensemble for Active Learning,
ACCV22(VI:493-509).
Springer DOI
2307
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
Wang, Z.M.[Zeng-Mao],
Chen, Z.X.[Zi-Xi],
Du, B.[Bo],
Active Learning With Co-Auxiliary Learning and Multi-Level Diversity
for Image Classification,
CirSysVideo(33), No. 8, August 2023, pp. 3899-3911.
IEEE DOI
2308
Uncertainty, Redundancy, Labeling, Task analysis, Learning systems, Training,
Deep learning, Active learning, auxiliary learning, image classification
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
Li, D.P.[De-Peng],
Zeng, Z.G.[Zhi-Gang],
CRNet: A Fast Continual Learning Framework With Random Theory,
PAMI(45), No. 9, September 2023, pp. 10731-10744.
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
Frick, T.[Thomas],
Antognini, D.[Diego],
Rigotti, M.[Mattia],
Giurgiu, I.[Ioana],
Grewe, B.[Benjamin],
Malossi, C.[Cristiano],
Active Learning for Imbalanced Civil Infrastructure Data,
CVCivil22(283-298).
Springer DOI
2304
BibRef
Hedegaard, L.[Lukas],
Iosifidis, A.[Alexandros],
Continual Inference: A Library for Efficient Online Inference with Deep
Neural Networks in Pytorch,
CADK22(21-34).
Springer DOI
2304
BibRef
Osowiechi, D.[David],
Hakim, G.A.V.[Gustavo A. Vargas],
Noori, M.[Mehrdad],
Cheraghalikhani, M.[Milad],
Ben Ayed, I.[Ismail],
Desrosiers, C.[Christian],
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow,
WACV23(2125-2126)
IEEE DOI
2302
Training, Adaptation models, Head, Sensitivity,
Computational modeling, Predictive models, visual reasoning
BibRef
Petit, G.[Grégoire],
Popescu, A.[Adrian],
Schindler, H.[Hugo],
Picard, D.[David],
Delezoide, B.[Bertrand],
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental
Learning,
WACV23(3900-3909)
IEEE DOI
2302
Performance evaluation, Location awareness, Codes, Filtering,
Feature extraction, Generators, Stability analysis,
Vision + language and/or other modalities
BibRef
Chandra, D.S.[Dupati Srikar],
Varshney, S.[Sakshi],
Srijith, P.K.,
Gupta, S.I.[Sun-Il],
Continual Learning with Dependency Preserving Hypernetworks,
WACV23(2338-2347)
IEEE DOI
2302
Recurrent neural networks, Computational modeling, Task analysis,
Image classification,
visual reasoning)
BibRef
Saha, G.[Gobinda],
Roy, K.[Kaushik],
Saliency Guided Experience Packing for Replay in Continual Learning,
WACV23(5262-5272)
IEEE DOI
2302
Location awareness, Learning systems, Visualization,
Statistical analysis, Reinforcement learning,
visual reasoning)
BibRef
Lee, K.Y.[Kuan-Ying],
Zhong, Y.[Yuanyi],
Wang, Y.X.[Yu-Xiong],
Do Pre-trained Models Benefit Equally in Continual Learning?,
WACV23(6474-6482)
IEEE DOI
2302
Training, Systematics, Codes, Computational modeling, Pipelines,
Benchmark testing, Algorithms: Machine learning architectures,
and algorithms (including transfer)
BibRef
Jiang, J.[Jian],
Celiktutan, O.[Oya],
Neural Weight Search for Scalable Task Incremental Learning,
WACV23(1390-1399)
IEEE DOI
2302
Deep learning, Costs, Benchmark testing, Aerospace electronics,
Inference algorithms, Task analysis
BibRef
Feillet, E.[Eva],
Petit, G.[Grégoire],
Popescu, A.[Adrian],
Reyboz, M.[Marina],
Hudelot, C.[Céline],
AdvisIL - A Class-Incremental Learning Advisor,
WACV23(2399-2408)
IEEE DOI
2302
Learning systems, Adaptation models, Codes, Memory management,
Training data, Algorithms: Machine learning architectures, visual reasoning)
BibRef
Pan, Z.C.[Zi-Cheng],
Yu, X.H.[Xiao-Han],
Zhang, M.[Miaohua],
Gao, Y.S.[Yong-Sheng],
SSFE-Net: Self-Supervised Feature Enhancement for Ultra-Fine-Grained
Few-Shot Class Incremental Learning,
WACV23(6264-6273)
IEEE DOI
2302
Knowledge engineering, Visualization, Layout,
Self-supervised learning, Benchmark testing, Agriculture
BibRef
Krishnan, R.,
Balaprakash, P.[Prasanna],
Continual Learning via Dynamic Programming,
ICPR22(1350-1356)
IEEE DOI
2212
Partial differential equations, Benchmark testing,
Mathematical models, Dynamic programming, Bellman principle
BibRef
Bellitto, G.[Giovanni],
Pennisi, M.[Matteo],
Palazzo, S.[Simone],
Bonicelli, L.[Lorenzo],
Boschini, M.[Matteo],
Calderara, S.[Simone],
Effects of Auxiliary Knowledge on Continual Learning,
ICPR22(1357-1363)
IEEE DOI
2212
Training, Knowledge engineering, Neural networks, Streaming media,
Feature extraction, Data models, Task analysis
BibRef
Domoguen, J.K.L.[Jansen Keith L.],
Naval, P.C.[Prospero C.],
Dynamic Model-Agnostic Meta-Learning for Incremental Few-Shot
Learning,
ICPR22(4927-4933)
IEEE DOI
2212
Deep learning, Adaptation models, Particle separators, Prototypes,
Benchmark testing, Task analysis
BibRef
Hossain, M.S.[Md Sazzad],
Saha, P.[Pritom],
Chowdhury, T.F.[Townim Faisal],
Rahman, S.[Shafin],
Rahman, F.[Fuad],
Mohammed, N.[Nabeel],
Rethinking Task-Incremental Learning Baselines,
ICPR22(2771-2777)
IEEE DOI
2212
Point cloud compression, Knowledge engineering, Solid modeling,
Image recognition, Memory management
BibRef
Li, Y.[Yinan],
Luo, R.H.[Rong-Hua],
Huang, Z.M.[Zhen-Ming],
Class Incremental Learning based on Local Structure Constraints in
Feature Space,
ICPR22(2056-2062)
IEEE DOI
2212
Deep learning, Learning systems, Degradation, Training data,
Stability analysis, Robustness, Classification algorithms
BibRef
Buchert, F.[Felix],
Navab, N.[Nassir],
Kim, S.T.[Seong Tae],
Exploiting Diversity of Unlabeled Data for Label-Efficient
Semi-Supervised Active Learning,
ICPR22(2063-2069)
IEEE DOI
2212
Training, Representation learning, Deep learning, Neural networks,
Self-supervised learning, Semisupervised learning
BibRef
Flesca, S.[Sergio],
Mandaglio, D.[Domenico],
Scala, F.[Francesco],
Tagarelli, A.[Andrea],
Learning to Active Learn by Gradient Variation based on Instance
Importance,
ICPR22(2224-2230)
IEEE DOI
2212
Deep learning, Annotations, Source coding, Current measurement,
Neural networks, Predictive models
BibRef
Wang, R.[Runqi],
Bao, Y.X.[Yu-Xiang],
Zhang, B.C.[Bao-Chang],
Liu, J.Z.[Jian-Zhuang],
Zhu, W.T.[Wen-Tao],
Guo, G.D.[Guo-Dong],
Anti-retroactive Interference for Lifelong Learning,
ECCV22(XXIV:163-178).
Springer DOI
2211
BibRef
Chen, Z.Z.[Zhuang-Zhuang],
Zhang, J.[Jin],
Wang, P.[Pan],
Chen, J.[Jie],
Li, J.Q.[Jian-Qiang],
When Active Learning Meets Implicit Semantic Data Augmentation,
ECCV22(XXV:56-72).
Springer DOI
2211
BibRef
Yi, J.S.K.[John Seon Keun],
Seo, M.[Minseok],
Park, J.[Jongchan],
Choi, D.G.[Dong-Geol],
PT4AL: Using Self-supervised Pretext Tasks for Active Learning,
ECCV22(XXVI:596-612).
Springer DOI
2211
BibRef
Sun, Y.Q.[Yong-Qing],
Qin, A.[Anyong],
Bandoh, Y.[Yukihiro],
Gao, C.Q.[Chen-Qiang],
Hiwasaki, Y.[Yusuke],
Active Learning for Hyperspectral Image Classification via Hypergraph
Neural Network,
ICIP22(2576-2580)
IEEE DOI
2211
Training, Convolution, Neural networks, Labeling, Faces,
Hyperspectral imaging, Hyperspectral Image Classification,
Graph Convolution Network
BibRef
Hyder, R.[Rakib],
Shao, K.[Ken],
Hou, B.[Boyu],
Markopoulos, P.[Panos],
Prater-Bennette, A.[Ashley],
Asif, M.S.[M. Salman],
Incremental Task Learning with Incremental Rank Updates,
ECCV22(XXIII:566-582).
Springer DOI
2211
BibRef
Rios, A.[Amanda],
Ahuja, N.[Nilesh],
Ndiour, I.[Ibrahima],
Genc, U.[Utku],
Itti, L.[Laurent],
Tickoo, O.[Omesh],
incDFM: Incremental Deep Feature Modeling for Continual Novelty
Detection,
ECCV22(XXV:588-604).
Springer DOI
2211
BibRef
He, J.P.[Jiang-Peng],
Zhu, F.Q.[Feng-Qing],
Exemplar-Free Online Continual Learning,
ICIP22(541-545)
IEEE DOI
2211
Training, Privacy, Protocols, Benchmark testing, Task analysis, Tuning,
Continual learning, Online scenario, Exemplar-free, Image classification
BibRef
Michel, N.[Nicolas],
Negrel, R.[Romain],
Chierchia, G.[Giovanni],
Bercher, J.F.[Jean-Fmnçois],
Contrastive Learning for Online Semi-Supervised General Continual
Learning,
ICIP22(1896-1900)
IEEE DOI
2211
Training, Memory management, Continual Learning,
Contrastive Learning, Semi-Supervised Learning, Memory
BibRef
Ye, F.[Fei],
Bors, A.G.[Adrian G.],
Learning an Evolved Mixture Model for Task-Free Continual Learning,
ICIP22(1936-1940)
IEEE DOI
2211
Training, Deep learning, Adaptation models, Mixture models,
Network architecture, Benchmark testing,
Hilbert Schmidt Independence Criterion
BibRef
Guimeng, L.[Liu],
Yang, G.[Guo],
Yin, C.W.S.[Cheryl Wong Sze],
Suganathan, P.N.[Ponnuthurai Nagartnam],
Savitha, R.[Ramasamy],
Unsupervised Generative Variational Continual Learning,
ICIP22(4028-4032)
IEEE DOI
2211
Training, Adaptation models, Uncertainty, Image coding, Neurons,
Benchmark testing, Task analysis, Continual Learning, Unsupervised,
Variational Inference
BibRef
Ashok, A.[Arjun],
Joseph, K.J.,
Balasubramanian, V.N.[Vineeth N.],
Class-Incremental Learning with Cross-Space Clustering and Controlled
Transfer,
ECCV22(XXVII:105-122).
Springer DOI
2211
BibRef
Kalla, J.[Jayateja],
Biswas, S.[Soma],
S3C: Self-Supervised Stochastic Classifiers for Few-Shot
Class-Incremental Learning,
ECCV22(XXV:432-448).
Springer DOI
2211
BibRef
Peng, C.[Can],
Zhao, K.[Kun],
Wang, T.R.[Tian-Ren],
Li, M.[Meng],
Lovell, B.C.[Brian C.],
Few-Shot Class-Incremental Learning from an Open-Set Perspective,
ECCV22(XXV:382-397).
Springer DOI
2211
BibRef
Wang, F.Y.[Fu-Yun],
Zhou, D.W.[Da-Wei],
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
FOSTER: Feature Boosting and Compression for Class-Incremental Learning,
ECCV22(XXV:398-414).
Springer DOI
2211
BibRef
Gao, Q.[Qiankun],
Zhao, C.[Chen],
Ghanem, B.[Bernard],
Zhang, J.[Jian],
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class
Incremental Learning,
ECCV22(XXIII:423-439).
Springer DOI
2211
BibRef
Findik, Y.[Yasin],
Pourkamali-Anaraki, F.[Farhad],
D-CBRS: Accounting for Intra-Class Diversity in Continual Learning,
ICIP22(2531-2535)
IEEE DOI
2211
Memory management, Reservoirs, Data models, Continual learning,
lifelong learning, catastrophic forgetting, class-incremental learning
BibRef
Liu, X.L.[Xia-Lei],
Hu, Y.S.[Yu-Song],
Cao, X.S.[Xu-Sheng],
Bagdanov, A.D.[Andrew D.],
Li, K.[Ke],
Cheng, M.M.[Ming-Ming],
Long-Tailed Class Incremental Learning,
ECCV22(XXXIII:495-512).
Springer DOI
2211
BibRef
Kothawade, S.[Suraj],
Ghosh, S.[Saikat],
Shekhar, S.[Sumit],
Xiang, Y.[Yu],
Iyer, R.[Rishabh],
Talisman: Targeted Active Learning for Object Detection with Rare
Classes and Slices Using Submodular Mutual Information,
ECCV22(XXXVIII:1-16).
Springer DOI
2211
BibRef
Pourcel, J.[Julien],
Vu, N.S.[Ngoc-Son],
French, R.M.[Robert M.],
Online Task-free Continual Learning with Dynamic Sparse Distributed
Memory,
ECCV22(XXV:739-756).
Springer DOI
2211
BibRef
Kong, Y.J.[Ya-Jing],
Liu, L.[Liu],
Wang, Z.[Zhen],
Tao, D.C.[Da-Cheng],
Balancing Stability and Plasticity Through Advanced Null Space in
Continual Learning,
ECCV22(XXVI:219-236).
Springer DOI
2211
BibRef
Wang, L.Y.[Li-Yuan],
Zhang, X.X.[Xing-Xing],
Li, Q.[Qian],
Zhu, J.[Jun],
Zhong, Y.[Yi],
CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big
One,
ECCV22(XXVI:254-271).
Springer DOI
2211
BibRef
Purushwalkam, S.[Senthil],
Morgado, P.[Pedro],
Gupta, A.[Abhinav],
The Challenges of Continuous Self-Supervised Learning,
ECCV22(XXVI:702-721).
Springer DOI
2211
BibRef
Shon, H.[Hyounguk],
Lee, J.[Janghyeon],
Kim, S.H.[Seung Hwan],
Kim, J.[Junmo],
DLCFT: Deep Linear Continual Fine-Tuning for General Incremental
Learning,
ECCV22(XXXIII:513-529).
Springer DOI
2211
BibRef
Wang, Z.F.[Zi-Feng],
Zhang, Z.Z.[Zi-Zhao],
Ebrahimi, S.[Sayna],
Sun, R.[Ruoxi],
Zhang, H.[Han],
Lee, C.Y.[Chen-Yu],
Ren, X.Q.[Xiao-Qi],
Su, G.L.[Guo-Long],
Perot, V.[Vincent],
Dy, J.[Jennifer],
Pfister, T.[Tomas],
DualPrompt: Complementary Prompting for Rehearsal-Free Continual
Learning,
ECCV22(XXVI:631-648).
Springer DOI
2211
BibRef
Hedegaard, L.[Lukas],
Iosifidis, A.[Alexandros],
Continual 3D Convolutional Neural Networks for Real-time Processing of
Videos,
ECCV22(IV:369-385).
Springer DOI
2211
BibRef
Jin, H.[Hyundong],
Kim, E.[Eunwoo],
Helpful or Harmful: Inter-task Association in Continual Learning,
ECCV22(XI:519-535).
Springer DOI
2211
BibRef
Andle, J.[Joshua],
Sekeh, S.Y.[Salimeh Yasaei],
Theoretical Understanding of the Information Flow on Continual Learning
Performance,
ECCV22(XII:86-101).
Springer DOI
2211
BibRef
Campari, T.[Tommaso],
Lamanna, L.[Leonardo],
Traverso, P.[Paolo],
Serafini, L.[Luciano],
Ballan, L.[Lamberto],
Online Learning of Reusable Abstract Models for Object Goal
Navigation,
CVPR22(14850-14859)
IEEE DOI
2210
Image segmentation, Navigation, Computational modeling,
Machine vision, Robot vision systems, Benchmark testing,
Vision applications and systems
BibRef
Araujo, V.[Vladimir],
Hurtado, J.[Julio],
Soto, A.[Alvaro],
Moens, M.F.[Marie-Francine],
Entropy-based Stability-Plasticity for Lifelong Learning,
CLVision22(3720-3727)
IEEE DOI
2210
Training, Deep learning, Computational modeling, Natural languages,
Neural networks, Interference, Transformers
BibRef
Wang, C.[Chen],
Qiu, Y.H.[Yu-Heng],
Gao, D.[Dasong],
Scherer, S.[Sebastian],
Lifelong Graph Learning,
CVPR22(13709-13718)
IEEE DOI
2210
Bridges, Training, Performance evaluation, Network topology,
Wearable computers, Graph neural networks, Topology, Statistical methods
BibRef
Yu, W.P.[Wei-Ping],
Zhu, S.[Sijie],
Yang, T.[Taojiannan],
Chen, C.[Chen],
Consistency-based Active Learning for Object Detection,
L3D-IVU22(3950-3959)
IEEE DOI
2210
Learning systems, Measurement, Object detection,
Detectors, Pattern recognition
BibRef
Parvaneh, A.[Amin],
Abbasnejad, E.[Ehsan],
Teney, D.[Damien],
Haffari, R.[Reza],
van den Hengel, A.J.[Anton J.],
Shi, J.Q.F.[Javen Qin-Feng],
Active Learning by Feature Mixing,
CVPR22(12227-12236)
IEEE DOI
2210
Interpolation, Costs, Codes, Machine vision, Predictive models,
Transformers, Efficient learning and inferences, Vision applications and systems
BibRef
Munjal, P.[Prateek],
Hayat, N.[Nasir],
Hayat, M.[Munawar],
Sourati, J.[Jamshid],
Khan, S.[Shadab],
Towards Robust and Reproducible Active Learning using Neural Networks,
CVPR22(223-232)
IEEE DOI
2210
Measurement, Uncertainty, Costs, Codes, Annotations, Neural networks,
Machine learning, Efficient learning and inferences,
privacy and ethics in vision
BibRef
Wu, J.X.[Jia-Xi],
Chen, J.X.[Jia-Xin],
Huang, D.[Di],
Entropy-based Active Learning for Object Detection with Progressive
Diversity Constraint,
CVPR22(9387-9396)
IEEE DOI
2210
Learning systems, Uncertainty, Costs, Prototypes, Object detection,
Entropy, Recognition: detection, categorization, retrieval
BibRef
Lin, G.L.[Guo-Liang],
Chu, H.[Hanlu],
Lai, H.J.[Han-Jiang],
Towards Better Plasticity-Stability Trade-off in Incremental
Learning: A Simple Linear Connector,
CVPR22(89-98)
IEEE DOI
2210
Connectors, Knowledge engineering, Upper bound, Codes,
Neural networks, Training data, Machine learning,
Transfer/low-shot/long-tail learning
BibRef
Bhunia, A.K.[Ayan Kumar],
Gajjala, V.R.[Viswanatha Reddy],
Koley, S.[Subhadeep],
Kundu, R.[Rohit],
Sain, A.[Aneeshan],
Xiang, T.[Tao],
Song, Y.Z.[Yi-Zhe],
Doodle It Yourself: Class Incremental Learning by Drawing a Few
Sketches,
CVPR22(2283-2292)
IEEE DOI
2210
Knowledge engineering, Ethics, Visualization,
Technological innovation, Privacy, Data privacy, Message passing,
Vision applications and systems
BibRef
Joseph, K.J.,
Khan, S.[Salman],
Khan, F.S.[Fahad Shahbaz],
Anwer, R.M.[Rao Muhammad],
Balasubramanian, V.N.[Vineeth N],
Energy-based Latent Aligner for Incremental Learning,
CVPR22(7442-7451)
IEEE DOI
2210
Manifolds, Deep learning, Codes, Pipelines, Object detection,
Detectors, Recognition: detection, categorization, retrieval,
Transfer/low-shot/long-tail learning
BibRef
Cermelli, F.[Fabio],
Geraci, A.[Antonino],
Fontanel, D.[Dario],
Caputo, B.[Barbara],
Modeling Missing Annotations for Incremental Learning in Object
Detection,
CLVision22(3699-3709)
IEEE DOI
2210
Training, Annotations, Training data, Object detection,
Detectors, Predictive models
BibRef
Tiwari, R.[Rishabh],
Killamsetty, K.[Krishnateja],
Iyer, R.[Rishabh],
Shenoy, P.[Pradeep],
GCR: Gradient Coreset based Replay Buffer Selection for Continual
Learning,
CVPR22(99-108)
IEEE DOI
2210
Adaptation models, Data models, Pattern recognition, Task analysis,
Optimization, Machine learning, Computer vision theory,
Optimization methods
BibRef
Yan, Q.S.[Qing-Sen],
Gong, D.[Dong],
Liu, Y.H.[Yu-Hang],
van den Hengel, A.[Anton],
Shi, J.Q.F.[Javen Qin-Feng],
Learning Bayesian Sparse Networks with Full Experience Replay for
Continual Learning,
CVPR22(109-118)
IEEE DOI
2210
Correlation, Neurons, Interference, Machine learning, Reservoirs,
Bayes methods, Machine learning, Deep learning architectures and techniques
BibRef
Wang, Z.[Zifeng],
Zhang, Z.[Zizhao],
Lee, C.Y.[Chen-Yu],
Zhang, H.[Han],
Sun, R.[Ruoxi],
Ren, X.Q.[Xiao-Qi],
Su, G.[Guolong],
Perot, V.[Vincent],
Dy, J.[Jennifer],
Pfister, T.[Tomas],
Learning to Prompt for Continual Learning,
CVPR22(139-149)
IEEE DOI
2210
Representation learning, Adaptation models, Codes,
Predictive models, Data models, Pattern recognition,
Representation learning
BibRef
Xue, M.Q.[Meng-Qi],
Zhang, H.[Haofei],
Song, J.[Jie],
Song, M.L.[Ming-Li],
Meta-attention for ViT-backed Continual Learning,
CVPR22(150-159)
IEEE DOI
2210
Learning systems, Degradation, Codes, Transformers,
Pattern recognition, Convolutional neural networks,
Deep learning architectures and techniques
BibRef
Wang, Z.[Zhen],
Liu, L.[Liu],
Kong, Y.J.[Ya-Jing],
Guo, J.X.[Jia-Xian],
Tao, D.C.[Da-Cheng],
Online Continual Learning with Contrastive Vision Transformer,
ECCV22(XX:631-650).
Springer DOI
2211
BibRef
Wang, Z.[Zhen],
Liu, L.[Liu],
Duan, Y.Q.[Yi-Qun],
Kong, Y.J.[Ya-Jing],
Tao, D.C.[Da-Cheng],
Continual Learning with Lifelong Vision Transformer,
CVPR22(171-181)
IEEE DOI
2210
Training, Learning systems, Neural networks, Interference,
Benchmark testing, Transformers, Machine learning, Others,
Representation learning
BibRef
Gu, Y.[Yanan],
Yang, X.[Xu],
Wei, K.[Kun],
Deng, C.[Cheng],
Not Just Selection, but Exploration: Online Class-Incremental
Continual Learning via Dual View Consistency,
CVPR22(7432-7441)
IEEE DOI
2210
Training, Representation learning, Semantics, Neural networks,
Benchmark testing, Streaming media, Recognition: detection,
Representation learning
BibRef
Simon, C.[Christian],
Faraki, M.[Masoud],
Tsai, Y.H.[Yi-Hsuan],
Yu, X.[Xiang],
Schulter, S.[Samuel],
Suh, Y.[Yumin],
Harandi, M.[Mehrtash],
Chandraker, M.[Manmohan],
On Generalizing Beyond Domains in Cross-Domain Continual Learning,
CVPR22(9255-9264)
IEEE DOI
2210
Knowledge engineering, Measurement, Deep learning,
Computational modeling, Neural networks, Pattern recognition, Machine learning
BibRef
Bang, J.[Jihwan],
Koh, H.[Hyunseo],
Park, S.[Seulki],
Song, H.[Hwanjun],
Ha, J.W.[Jung-Woo],
Choi, J.H.[Jong-Hyun],
Online Continual Learning on a Contaminated Data Stream with Blurry
Task Boundaries,
CVPR22(9265-9274)
IEEE DOI
2210
Art, Codes, Memory management, Semisupervised learning,
Pattern recognition, Noise measurement, Self- semi- meta- unsupervised learning
BibRef
Douillard, A.[Arthur],
Ramé, A.[Alexandre],
Couairon, G.[Guillaume],
Cord, M.[Matthieu],
DyTox: Transformers for Continual Learning with DYnamic TOken
eXpansion,
CVPR22(9275-9285)
IEEE DOI
2210
Representation learning, Deep learning, Memory management,
Network architecture, Transformers, Market research, Decoding,
Representation learning
BibRef
Zhu, K.[Kai],
Zhai, W.[Wei],
Cao, Y.[Yang],
Luo, J.B.[Jie-Bo],
Zha, Z.J.[Zheng-Jun],
Self-Sustaining Representation Expansion for Non-Exemplar
Class-Incremental Learning,
CVPR22(9286-9295)
IEEE DOI
2210
Fuses, Prototypes, Benchmark testing, Pattern recognition,
Task analysis, Optimization,
Representation learning
BibRef
Fini, E.[Enrico],
da Costa, V.G.T.[Victor G. Turrisi],
Alameda-Pineda, X.[Xavier],
Ricci, E.[Elisa],
Alahari, K.[Karteek],
Mairal, J.[Julien],
Self-Supervised Models are Continual Learners,
CVPR22(9611-9620)
IEEE DOI
2210
Training, Representation learning, Visualization, Surveillance,
Self-supervised learning, Data models, Self- semi- meta- Representation learning
BibRef
Chen, G.[Geng],
Zhang, W.D.[Wen-Dong],
Lu, H.[Han],
Gao, S.[Siyu],
Wang, Y.[Yunbo],
Long, M.S.[Ming-Sheng],
Yang, X.K.[Xiao-Kang],
Continual Predictive Learning from Videos,
CVPR22(10718-10727)
IEEE DOI
2210
Training, Adaptation models, Art, Predictive models,
Benchmark testing, Prediction algorithms,
Self- semi- meta- unsupervised learning
BibRef
Wan, T.S.T.[Timmy S. T.],
Chen, J.C.[Jun-Cheng],
Wu, T.Y.[Tzer-Yi],
Chen, C.S.[Chu-Song],
Continual Learning for Visual Search with Backward Consistent Feature
Embedding,
CVPR22(16681-16690)
IEEE DOI
2210
Representation learning, Visualization, Computational modeling,
Coherence, Benchmark testing, Data models, Representation learning,
retrieval
BibRef
Davari, M.R.[Mohammad-Reza],
Asadi, N.[Nader],
Mudur, S.[Sudhir],
Aljundi, R.[Rahaf],
Belilovsky, E.[Eugene],
Probing Representation Forgetting in Supervised and Unsupervised
Continual Learning,
CVPR22(16691-16700)
IEEE DOI
2210
Representation learning, Training, Measurement,
Supervised learning, Neural networks, Prototypes, Representation learning
BibRef
Shi, Y.J.[Yu-Jun],
Zhou, K.Q.[Kuang-Qi],
Liang, J.[Jian],
Jiang, Z.[Zihang],
Feng, J.S.[Jia-Shi],
Torr, P.H.S.[Philip H.S.],
Bai, S.[Song],
Tan, V.Y.F.[Vincent Y.F.],
Mimicking the Oracle: An Initial Phase Decorrelation Approach for
Class Incremental Learning,
CVPR22(16701-16710)
IEEE DOI
2210
Training, Representation learning, Codes, Computational modeling,
Benchmark testing, Pattern recognition, Representation learning,
retrieval
BibRef
Taufique, A.M.N.[Abu Md Niamul],
Jahan, C.S.[Chowdhury Sadman],
Savakis, A.[Andreas],
Unsupervised Continual Learning for Gradually Varying Domains,
CLVision22(3739-3749)
IEEE DOI
2210
Learning systems, Bridges, Adaptation models, Codes, Memory management
BibRef
Ermis, B.[Beyza],
Zappella, G.[Giovanni],
Wistuba, M.[Martin],
Rawal, A.[Aditya],
Archambeau, C.[Cédric],
Continual Learning with Transformers for Image Classification,
CLVision22(3773-3780)
IEEE DOI
2210
Training, Adaptation models, Computational modeling,
Neural networks, Training data, Predictive models
BibRef
Carta, A.[Antonio],
Cossu, A.[Andrea],
Lomonaco, V.[Vincenzo],
Bacciu, D.[Davide],
Ex-Model: Continual Learning from a Stream of Trained Models,
CLVision22(3789-3798)
IEEE DOI
2210
Learning systems, Data privacy,
Computational modeling, Data models, Pattern recognition
BibRef
Jie, S.[Shibo],
Deng, Z.H.[Zhi-Hong],
Li, Z.H.[Zi-Heng],
Alleviating Representational Shift for Continual Fine-tuning,
CLVision22(3809-3818)
IEEE DOI
2210
Training, Pattern recognition, Task analysis, Testing
BibRef
Pelosin, F.[Francesco],
Jha, S.[Saurav],
Torsello, A.[Andrea],
Raducanu, B.[Bogdan],
van de Weijer, J.[Joost],
Towards Exemplar-Free Continual Learning in Vision Transformers: an
Account of Attention, Functional and Weight Regularization,
CLVision22(3819-3828)
IEEE DOI
2210
Learning systems, Weight measurement, Image recognition, Surgery,
Benchmark testing, Transformers, Stability analysis
BibRef
He, J.P.[Jiang-Peng],
Zhu, F.Q.[Feng-Qing],
Out-Of-Distribution Detection In Unsupervised Continual Learning,
CLVision22(3849-3854)
IEEE DOI
2210
Protocols, Annotations, Detectors, Pattern recognition, Task analysis
BibRef
Kim, G.[Gyuhak],
Esmaeilpour, S.[Sepideh],
Xiao, C.[Changnan],
Liu, B.[Bing],
Continual Learning Based on OOD Detection and Task Masking,
CLVision22(3855-3865)
IEEE DOI
2210
Training, Machine learning algorithms, Codes,
Supervised learning, Data models
BibRef
Gomez-Villa, A.[Alex],
Twardowski, B.[Bartlomiej],
Yu, L.[Lu],
Bagdanov, A.D.[Andrew D.],
van de Weijer, J.[Joost],
Continually Learning Self-Supervised Representations with Projected
Functional Regularization,
CLVision22(3866-3876)
IEEE DOI
2210
Conferences, Self-supervised learning, Image representation, Pattern recognition
BibRef
Karim, N.[Nazmul],
Khalid, U.[Umar],
Esmaeili, A.[Ashkan],
Rahnavard, N.[Nazanin],
CNLL: A Semi-supervised Approach For Continual Noisy Label Learning,
CLVision22(3877-3887)
IEEE DOI
2210
Training, Codes, Purification, Benchmark testing, Performance gain
BibRef
Merlin, G.[Gabriele],
Lomonaco, V.[Vincenzo],
Cossu, A.[Andrea],
Carta, A.[Antonio],
Bacciu, D.[Davide],
Practical Recommendations for Replay-Based Continual Learning Methods,
CL4REAL22(548-559).
Springer DOI
2208
BibRef
Kim, S.[Sohee],
Lee, S.K.[Seung-Kyu],
Continual Learning with Neuron Activation Importance,
CIAP22(I:310-321).
Springer DOI
2205
BibRef
Lucchesi, N.[Nicoló],
Carta, A.[Antonio],
Lomonaco, V.[Vincenzo],
Bacciu, D.[Davide],
Avalanche RL: A Continual Reinforcement Learning Library,
CIAP22(I:524-535).
Springer DOI
2205
BibRef
Barletti, T.[Tommaso],
Biondi, N.[Niccoló],
Pernici, F.[Federico],
Bruni, M.[Matteo],
del Bimbo, A.[Alberto],
Contrastive Supervised Distillation for Continual Representation
Learning,
CIAP22(I:597-609).
Springer DOI
2205
BibRef
Davalas, C.[Charalampos],
Michail, D.[Dimitrios],
Diou, C.[Christos],
Varlamis, I.[Iraklis],
Tserpes, K.[Konstantinos],
Computationally Efficient Rehearsal for Online Continual Learning,
CIAP22(III:39-49).
Springer DOI
2205
BibRef
Zhong, Y.J.[Yi-Jie],
Sun, Z.X.[Zheng-Xing],
Luo, S.T.[Shou-Tong],
Sun, Y.H.[Yun-Han],
Zhang, W.[Wei],
Category-Sensitive Incremental Learning for Image-Based 3D Shape
Reconstruction,
MMMod22(I:231-244).
Springer DOI
2203
BibRef
Guo, J.N.[Jian-Nan],
hi, H.C.S.[Hao-Chen S],
Kang, Y.Y.[Yang-Yang],
Kuang, K.[Kun],
Tang, S.L.[Si-Liang],
Jiang, Z.R.[Zhuo-Ren],
Sun, C.L.[Chang-Long],
Wu, F.[Fei],
Zhuang, Y.T.[Yue-Ting],
Semi-supervised Active Learning for Semi-supervised Models: Exploit
Adversarial Examples with Graph-based Virtual Labels,
ICCV21(2876-2885)
IEEE DOI
2203
Costs, Computational modeling, Clustering algorithms,
Semisupervised learning, Rendering (computer graphics),
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Huang, S.[Siyu],
Wang, T.Y.[Tian-Yang],
Xiong, H.Y.[Hao-Yi],
Huan, J.[Jun],
Dou, D.[Dejing],
Semi-Supervised Active Learning with Temporal Output Discrepancy,
ICCV21(3427-3436)
IEEE DOI
2203
Training, Image segmentation, Annotations, Semantics,
Loss measurement, Data models, Task analysis,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Liu, Z.M.[Zhuo-Ming],
Ding, H.[Hao],
Zhong, H.P.[Hua-Ping],
Li, W.J.[Wei-Jia],
Dai, J.F.[Ji-Feng],
He, C.H.[Cong-Hui],
Influence Selection for Active Learning,
ICCV21(9254-9263)
IEEE DOI
2203
Learning systems, Costs, Uncertainty, Annotations,
Computational modeling, Neural networks,
Recognition and classification
BibRef
Choi, J.[Jiwoong],
Elezi, I.[Ismail],
Lee, H.J.[Hyuk-Jae],
Farabet, C.[Clement],
Alvarez, J.M.[Jose M.],
Active Learning for Deep Object Detection via Probabilistic Modeling,
ICCV21(10244-10253)
IEEE DOI
2203
Location awareness, Uncertainty, Costs, Head, Computational modeling,
Object detection, Performance gain, Representation learning,
Detection and localization in 2D and 3D
BibRef
Smith, J.[James],
Hsu, Y.C.[Yen-Chang],
Balloch, J.[Jonathan],
Shen, Y.[Yilin],
Jin, H.X.[Hong-Xia],
Kira, Z.[Zsolt],
Always Be Dreaming:
A New Approach for Data-Free Class-Incremental Learning,
ICCV21(9354-9364)
IEEE DOI
2203
Training, Learning systems, Law, Memory management, Training data,
Benchmark testing,
Recognition and classification
BibRef
Wu, G.[Guile],
Gong, S.G.[Shao-Gang],
Queen, P.L.[Pan Li],
Striking a Balance between Stability and Plasticity for
Class-Incremental Learning,
ICCV21(1104-1113)
IEEE DOI
2203
Bridges, Computational modeling, Benchmark testing,
Stability analysis, Recognition and classification,
Optimization and learning methods
BibRef
Ahn, H.[Hongjoon],
Kwak, J.[Jihwan],
Lim, S.B.[Su-Bin],
Bang, H.[Hyeonsu],
Kim, H.[Hyojun],
Moon, T.[Taesup],
SS-IL: Separated Softmax for Incremental Learning,
ICCV21(824-833)
IEEE DOI
2203
Systematics, Training data, Benchmark testing, Task analysis,
Recognition and classification,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Yan, Z.[Zike],
Tian, Y.X.[Yu-Xin],
Shi, X.S.[Xue-Song],
Guo, P.[Ping],
Wang, P.[Peng],
Zha, H.B.[Hong-Bin],
Continual Neural Mapping: Learning An Implicit Scene Representation
from Sequential Observations,
ICCV21(15762-15772)
IEEE DOI
2203
Geometry, Robot kinematics, Neural networks, Streaming media,
Network architecture, Real-time systems,
Scene analysis and understanding
BibRef
Kim, C.D.[Chris Dongjoo],
Jeong, J.[Jinseo],
Moon, S.[Sangwoo],
Kim, G.[Gunhee],
Continual Learning on Noisy Data Streams via Self-Purified Replay,
ICCV21(517-527)
IEEE DOI
2203
Training, Heart, Buildings, Information filters, Noise measurement,
Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
de Lange, M.[Matthias],
Tuytelaars, T.[Tinne],
Continual Prototype Evolution:
Learning Online from Non-Stationary Data Streams,
ICCV21(8230-8239)
IEEE DOI
2203
Training, Representation learning, Memory management, Prototypes,
Benchmark testing, Linear programming, Synchronization,
Representation learning
BibRef
Verwimp, E.[Eli],
de Lange, M.[Matthias],
Tuytelaars, T.[Tinne],
Rehearsal revealed:
The limits and merits of revisiting samples in continual learning,
ICCV21(9365-9374)
IEEE DOI
2203
Computational modeling, Machine learning, Benchmark testing,
Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning,
Recognition and classification
BibRef
Cha, H.[Hyuntak],
Lee, J.[Jaeho],
Shin, J.[Jinwoo],
Co2L: Contrastive Continual Learning,
ICCV21(9496-9505)
IEEE DOI
2203
Representation learning, Visualization, Codes,
Computational modeling, Benchmark testing,
Representation learning
BibRef
Cai, Z.P.[Zhi-Peng],
Sener, O.[Ozan],
Koltun, V.[Vladlen],
Online Continual Learning with Natural Distribution Shifts:
An Empirical Study with Visual Data,
ICCV21(8261-8270)
IEEE DOI
2203
Training, Measurement, Visualization, Schedules, Supervised learning,
Coherence, Benchmark testing,
Vision + other modalities
BibRef
Lee, E.[Eugene],
Huang, C.H.[Cheng-Han],
Lee, C.Y.[Chen-Yi],
Few-Shot and Continual Learning with Attentive Independent Mechanisms,
ICCV21(9435-9444)
IEEE DOI
2203
Training, Deep learning, Adaptation models, Codes, Art,
Computational modeling,
Visual reasoning and logical representation
BibRef
Kukleva, A.[Anna],
Kuehne, H.[Hilde],
Schiele, B.[Bernt],
Generalized and Incremental Few-Shot Learning by Explicit Learning
and Calibration without Forgetting,
ICCV21(9000-9009)
IEEE DOI
2203
Training, Deep learning, Benchmark testing, Entropy, Calibration,
Task analysis, Transfer/Low-shot/Semi/Unsupervised Learning, Recognition and classification
BibRef
Cheraghian, A.[Ali],
Rahman, S.[Shafin],
Ramasinghe, S.[Sameera],
Fang, P.F.[Peng-Fei],
Simon, C.[Christian],
Petersson, L.[Lars],
Harandi, M.[Mehrtash],
Synthesized Feature based Few-Shot Class-Incremental Learning on a
Mixture of Subspaces,
ICCV21(8641-8650)
IEEE DOI
2203
Training, Visualization, Adaptation models, Computational modeling,
Semantics, Prototypes,
BibRef
Bengar, J.Z.[Javad Zolfaghari],
van de Weijer, J.[Joost],
Fuentes, L.L.[Laura Lopez],
Raducanu, B.[Bogdan],
Class-Balanced Active Learning for Image Classification,
WACV22(3707-3716)
IEEE DOI
2202
Learning systems, Performance gain,
Classification algorithms, Labeling, Optimization, Learning and Optimization
BibRef
Aljundi, R.[Rahaf],
Chumerin, N.[Nikolay],
Reino, D.O.[Daniel Olmeda],
Identifying Wrongly Predicted Samples: A Method for Active Learning,
WACV22(2071-2079)
IEEE DOI
2202
Learning systems, Uncertainty, Systematics, Limiting, Annotations,
Computational modeling, Predictive models, Deep Learning Active Learning
BibRef
Gopalakrishnan, S.[Saisubramaniam],
Singh, P.R.[Pranshu Ranjan],
Fayek, H.[Haytham],
Ramasamy, S.[Savitha],
Ambikapathi, A.M.[Arul-Murugan],
Knowledge Capture and Replay for Continual Learning,
WACV22(337-345)
IEEE DOI
2202
Training, Deep learning, Visualization, Data privacy,
Noise reduction, Neural networks, Knowledge representation,
Semi- and Un- supervised Learning Continual Learning
BibRef
He, J.P.[Jiang-Peng],
Zhu, F.Q.[Feng-Qing],
Online Continual Learning Via Candidates Voting,
WACV22(1292-1301)
IEEE DOI
2202
Training, Data privacy, Memory management,
Benchmark testing, Task analysis, Image classification,
Vision Systems and Applications
BibRef
Zhang, H.[Heng],
Fromont, E.[Elisa],
Lefevre, S.[Sébastien],
Avignon, B.[Bruno],
Deep Active Learning from Multispectral Data Through Cross-Modality
Prediction Inconsistency,
ICIP21(449-453)
IEEE DOI
2201
Image segmentation, Image analysis, Redundancy, Manuals,
Sensor fusion, Robustness, Sensors, Active learning,
multiple sensor fusion
BibRef
Pham, X.C.[Xuan Cuong],
Liew, A.W.C.[Alan Wee-Chung],
Wang, C.[Can],
A Novel Class-wise Forgetting Detector in Continual Learning,
DICTA21(01-08)
IEEE DOI
2201
Training, Learning systems, Deep learning, Adaptation models,
Digital images, Detectors, Data models, Online learning, Deep learning
BibRef
Singh, P.R.[Pranshu Ranjan],
Gopalakrishnan, S.[Saisubramaniam],
ZhongZheng, Q.[Qiao],
Suganthan, P.N.[Ponnuthurai N.],
Ramasamy, S.[Savitha],
Ambikapathi, A.[ArulMurugan],
Task-Agnostic Continual Learning Using Base-Child Classifiers,
ICIP21(794-798)
IEEE DOI
2201
Image processing, Complexity theory, Classification algorithms,
Task analysis, Standards, Continual Learning, Hybrid Networks
BibRef
Bengar, J.Z.[Javad Zolfaghari],
Raducanu, B.[Bogdan],
van de Weijer, J.[Joost],
When Deep Learners Change Their Mind: Learning Dynamics for Active
Learning,
CAIP21(I:403-413).
Springer DOI
2112
BibRef
Sreenivasaiah, D.[Deepthi],
Otterbach, J.[Johannes],
Wollmann, T.[Thomas],
MEAL: Manifold Embedding-based Active Learning,
ERCVAD21(1029-1037)
IEEE DOI
2112
Manifolds, Learning systems, Image segmentation, Uncertainty,
Measurement uncertainty, Training data, Entropy
BibRef
Bengar, J.Z.[Javad Zolfaghari],
van de Weijer, J.[Joost],
Twardowski, B.[Bartlomiej],
Raducanu, B.[Bogdan],
Reducing Label Effort: Self-Supervised meets Active Learning,
ILDAV21(1631-1639)
IEEE DOI
2112
Training, Annotations,
Supervised learning, Labeling, Object recognition
BibRef
Oren, G.[Guy],
Wolf, L.B.[Lior B.],
In Defense of the Learning Without Forgetting for Task Incremental
Learning,
DeepMTL21(2209-2218)
IEEE DOI
2112
Learning systems, Codes, Roads,
Solids
BibRef
Yan, Z.[Zike],
Wang, X.[Xin],
Zha, H.B.[Hong-Bin],
Online Learning of a Probabilistic and Adaptive Scene Representation,
CVPR21(13106-13116)
IEEE DOI
2111
Geometry, Adaptation models, Computational modeling,
Mixture models, Probability density function,
Data models
BibRef
Pang, B.[Bo],
Peng, G.[Gao],
Li, Y.Z.[Yi-Zhuo],
Lu, C.[Cewu],
PGT: A Progressive Method for Training Models on Long Videos,
CVPR21(11374-11384)
IEEE DOI
2111
Training, Convolutional codes,
Computational modeling, Video sequences, Semantics, Markov processes
BibRef
Wang, X.D.[Xu-Dong],
Lian, L.[Long],
Yu, S.X.[Stella X.],
Unsupervised Visual Attention and Invariance for Reinforcement
Learning,
CVPR21(6673-6683)
IEEE DOI
2111
Training, Visualization, Annotations,
Reinforcement learning, Manuals, Benchmark testing
BibRef
Singh, P.[Pravendra],
Mazumder, P.[Pratik],
Rai, P.[Piyush],
Namboodiri, V.P.[Vinay P.],
Rectification-based Knowledge Retention for Continual Learning,
CVPR21(15277-15286)
IEEE DOI
2111
Learning systems, Training, Deep learning,
Adaptation models, Pattern recognition, Task analysis
BibRef
Shi, Y.J.[Yu-Jun],
Yuan, L.[Li],
Chen, Y.P.[Yun-Peng],
Feng, J.S.[Jia-Shi],
Continual Learning via Bit-Level Information Preserving,
CVPR21(16669-16678)
IEEE DOI
2111
Quantization (signal), Costs, Neural networks, Memory management,
Reinforcement learning, Distance measurement, Pattern recognition
BibRef
Verma, V.K.[Vinay Kumar],
Liang, K.J.[Kevin J],
Mehta, N.[Nikhil],
Rai, P.[Piyush],
Carin, L.[Lawrence],
Efficient Feature Transformations for Discriminative and Generative
Continual Learning,
CVPR21(13860-13870)
IEEE DOI
2111
Learning systems, Computational modeling,
Scalability, Neural networks, Transforms, Predictive models
BibRef
Tang, S.X.[Shi-Xiang],
Chen, D.P.[Da-Peng],
Zhu, J.[Jinguo],
Yu, S.J.[Shi-Jie],
Ouyang, W.L.[Wan-Li],
Layerwise Optimization by Gradient Decomposition for Continual
Learning,
CVPR21(9629-9638)
IEEE DOI
2111
Knowledge engineering, Deep learning,
Computational modeling, Benchmark testing, Pattern recognition, Task analysis
BibRef
Wang, S.P.[Shi-Peng],
Li, X.R.[Xiao-Rong],
Sun, J.[Jian],
Xu, Z.B.[Zong-Ben],
Training Networks in Null Space of Feature Covariance for Continual
Learning,
CVPR21(184-193)
IEEE DOI
2111
Training, Null space, Benchmark testing,
Approximation algorithms, Stability analysis, Pattern recognition
BibRef
Volpi, R.[Riccardo],
Larlus, D.[Diane],
Rogez, G.[Grégory],
Continual Adaptation of Visual Representations via Domain
Randomization and Meta-learning,
CVPR21(4441-4451)
IEEE DOI
2111
Visualization, Adaptation models, Image segmentation,
Computational modeling, Semantics, Pattern recognition
BibRef
Bang, J.[Jihwan],
Kim, H.[Heesu],
Yoo, Y.J.[Young-Joon],
Ha, J.W.[Jung-Woo],
Choi, J.H.[Jong-Hyun],
Rainbow Memory: Continual Learning with a Memory of Diverse Samples,
CVPR21(8214-8223)
IEEE DOI
2111
Training, Uncertainty, Codes, Memory management,
Learning (artificial intelligence), Sampling methods
BibRef
Simon, C.[Christian],
Koniusz, P.[Piotr],
Harandi, M.[Mehrtash],
On Learning the Geodesic Path for Incremental Learning,
CVPR21(1591-1600)
IEEE DOI
2111
Manifolds, Knowledge engineering, Neural networks,
Linear programming, Pattern recognition, Task analysis
BibRef
Wu, Z.Y.[Zi-Yang],
Baek, C.[Christina],
You, C.[Chong],
Ma, Y.[Yi],
Incremental Learning via Rate Reduction,
CVPR21(1125-1133)
IEEE DOI
2111
Deep learning, Training, Backpropagation,
Computational modeling, Data models
BibRef
Liu, Y.Y.[Yao-Yao],
Schiele, B.[Bernt],
Sun, Q.[Qianru],
Adaptive Aggregation Networks for Class-Incremental Learning,
CVPR21(2544-2553)
IEEE DOI
2111
Adaptation models, Adaptive systems,
Network architecture,
Benchmark testing, Stability analysis
BibRef
Yan, S.P.[Shi-Peng],
Xie, J.W.[Jiang-Wei],
He, X.M.[Xu-Ming],
DER: Dynamically Expandable Representation for Class Incremental
Learning,
CVPR21(3013-3022)
IEEE DOI
2111
Visualization, Adaptation models,
Benchmark testing, Feature extraction, Pattern recognition, Complexity theory
BibRef
Hu, X.[Xinting],
Tang, K.[Kaihua],
Miao, C.Y.[Chun-Yan],
Hua, X.S.[Xian-Sheng],
Zhang, H.[Hanwang],
Distilling Causal Effect of Data in Class-Incremental Learning,
CVPR21(3956-3965)
IEEE DOI
2111
Training, Costs, Streaming media, Benchmark testing,
Pattern recognition, Reliability
BibRef
Wang, L.Y.[Li-Yuan],
Yang, K.[Kuo],
Li, C.X.[Chong-Xuan],
Hong, L.Q.[Lan-Qing],
Li, Z.G.[Zhen-Guo],
Zhu, J.[Jun],
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data
for Semi-supervised Continual Learning,
CVPR21(5379-5388)
IEEE DOI
2111
Deep learning, Systematics,
Semisupervised learning, Benchmark testing,
Generators
BibRef
Zhu, K.[Kai],
Cao, Y.[Yang],
Zhai, W.[Wei],
Cheng, J.[Jie],
Zha, Z.J.[Zheng-Jun],
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental
Learning,
CVPR21(6797-6806)
IEEE DOI
2111
Adaptation models, Computational modeling,
Prototypes, Benchmark testing, Power capacitors, Pattern recognition
BibRef
Abdelsalam, M.[Mohamed],
Faramarzi, M.[Mojtaba],
Sodhani, S.[Shagun],
Chandar, S.[Sarath],
IIRC: Incremental Implicitly-Refined Classification,
CVPR21(11033-11042)
IEEE DOI
2111
Analytical models, Computational modeling,
Benchmark testing, Prediction algorithms, Pattern recognition,
Classification algorithms
BibRef
Zhang, C.[Chi],
Song, N.[Nan],
Lin, G.S.[Guo-Sheng],
Zheng, Y.[Yun],
Pan, P.[Pan],
Xu, Y.H.[Ying-Hui],
Few-Shot Incremental Learning with Continually Evolved Classifiers,
CVPR21(12450-12459)
IEEE DOI
2111
Adaptation models, Machine learning algorithms,
Training data, Benchmark testing, Power capacitors, Pattern recognition
BibRef
Shukla, M.[Megh],
Ahmed, S.[Shuaib],
A Mathematical Analysis of Learning Loss for Active Learning in
Regression,
TCV21(3315-3323)
IEEE DOI
2109
Training, Industries, Fault diagnosis, Computational modeling,
Pose estimation, Refining, Mathematical analysis
BibRef
Rakesh, V.[Vineeth],
Jain, S.[Swayambhoo],
Efficacy of Bayesian Neural Networks in Active Learning,
LLID21(2601-2609)
IEEE DOI
2109
Uncertainty, Monte Carlo methods, Neural networks,
Estimation, Machine learning, Data models
BibRef
Masana, M.[Marc],
Tuytelaars, T.[Tinne],
van de Weijer, J.[Joost],
Ternary Feature Masks: zero-forgetting for task-incremental learning,
CLVision21(3565-3574)
IEEE DOI
2109
Scalability, Encoding,
Pattern recognition, Computational efficiency, Task analysis
BibRef
van de Ven, G.M.[Gido M.],
Li, Z.[Zhe],
Tolias, A.S.[Andreas S.],
Class-Incremental Learning with Generative Classifiers,
CLVision21(3606-3615)
IEEE DOI
2109
Training, Learning systems, Deep learning,
Monte Carlo methods, Benchmark testing
BibRef
Sun, W.J.[Wen-Ju],
Zhang, J.[Jing],
Wang, D.Y.[Dan-Yu],
Geng, Y.A.[Yangli-Ao],
Li, Q.Y.[Qing-Yong],
ILCOC: An Incremental Learning Framework based on Contrastive
One-class Classifiers,
CLVision21(3575-3583)
IEEE DOI
2109
Degradation, Heuristic algorithms,
Computational modeling, Pattern recognition, Classification algorithms
BibRef
Jiang, J.[Jian],
Cetin, E.[Edoardo],
Celiktutan, O.[Oya],
IB-DRR: Incremental Learning with Information-Back Discrete
Representation Replay,
CLVision21(3528-3537)
IEEE DOI
2109
Training, Image coding,
Memory management, Machine learning, Pattern recognition
BibRef
Mittal, S.[Sudhanshu],
Galesso, S.[Silvio],
Brox, T.[Thomas],
Essentials for Class Incremental Learning,
CLVision21(3508-3517)
IEEE DOI
2109
Learning systems, Art, Neural networks,
Training data, Boosting
BibRef
Douillard, A.[Arthur],
Valle, E.[Eduardo],
Ollion, C.[Charles],
Robert, T.[Thomas],
Cord, M.[Matthieu],
Insights from the Future for Continual Learning,
CLVision21(3477-3486)
IEEE DOI
2109
Training, Computational modeling,
Training data, Pattern recognition, Task analysis
BibRef
Hayes, T.L.[Tyler L.],
Kanan, C.[Christopher],
Selective Replay Enhances Learning in Online Continual Analogical
Reasoning,
CLVision21(3497-3507)
IEEE DOI
2109
Measurement, Protocols, Neural networks, Reinforcement learning,
Streaming media, Cognition, Pattern recognition
BibRef
Kuo, N.I.H.[Nicholas I-Hsien],
Harandi, M.[Mehrtash],
Fourrier, N.[Nicolas],
Walder, C.[Christian],
Ferraro, G.[Gabriela],
Suominen, H.[Hanna],
Plastic and Stable Gated Classifiers for Continual Learning,
CLVision21(3548-3553)
IEEE DOI
2109
Training, Knowledge engineering, Neural networks,
Logic gates, Feature extraction, Robustness
BibRef
Mai, Z.[Zheda],
Li, R.[Ruiwen],
Kim, H.W.[Hyun-Woo],
Sanner, S.[Scott],
Supervised Contrastive Replay: Revisiting the Nearest Class Mean
Classifier in Online Class-Incremental Continual Learning,
CLVision21(3584-3594)
IEEE DOI
2109
Training, Performance gain, Pattern recognition
BibRef
Lomonaco, V.[Vincenzo],
Pellegrini, L.[Lorenzo],
Cossu, A.[Andrea],
Carta, A.[Antonio],
Graffieti, G.[Gabriele],
Hayes, T.L.[Tyler L.],
de Lange, M.[Matthias],
Masana, M.[Marc],
Pomponi, J.[Jary],
van de Ven, G.M.[Gido M.],
Mundt, M.[Martin],
She, Q.[Qi],
Cooper, K.[Keiland],
Forest, J.[Jeremy],
Belouadah, E.[Eden],
Calderara, S.[Simone],
Parisi, G.I.[German I.],
Cuzzolin, F.[Fabio],
Tolias, A.S.[Andreas S.],
Scardapane, S.[Simone],
Antiga, L.[Luca],
Ahmad, S.[Subutai],
Popescu, A.[Adrian],
Kanan, C.[Christopher],
van de Weijer, J.[Joost],
Tuytelaars, T.[Tinne],
Bacciu, D.[Davide],
Maltoni, D.[Davide],
Avalanche: an End-to-End Library for Continual Learning,
CLVision21(3595-3605)
IEEE DOI
2109
Training, Deep learning,
Machine learning algorithms, Collaboration, Libraries
BibRef
Mirzadeh, S.I.[Seyed Iman],
Ghasemzadeh, H.[Hassan],
CL-Gym: Full-Featured PyTorch Library for Continual Learning,
OmniCV21(3616-3622)
IEEE DOI
2109
Philosophical considerations,
Learning (artificial intelligence),
Libraries
BibRef
Bagi, A.M.[Alexandra M.],
Schild, K.I.[Kim I.],
Khan, O.S.[Omar Shahbaz],
Zahálka, J.[Jan],
Jónsson, B.Þ.[Björn Þór],
XQM: Interactive Learning on Mobile Phones,
MMMod21(II:281-293).
Springer DOI
2106
BibRef
Shi, F.F.[Fei-Fei],
Wang, P.[Peng],
Shi, Z.C.[Zhong-Chao],
Rui, Y.[Yong],
Selecting Useful Knowledge from Previous Tasks for Future Learning in
a Single Network,
ICPR21(9727-9732)
IEEE DOI
2105
Knowledge engineering, Learning systems, Network architecture,
Iterative methods, Task analysis
BibRef
Jarboui, F.[Firas],
Perchet, V.[Vianney],
Trajectory representation learning for Multi-Task NMRDP planning,
ICPR21(6786-6793)
IEEE DOI
2105
Non Markovian Reward Decision Processes.
Bridges, Reinforcement learning, Markov processes, Trajectory,
Planning, Task analysis
BibRef
Lechat, A.[Alexis],
Herbin, S.[Stéphane],
Jurie, F.[Frédéric],
Semi-Supervised Class Incremental Learning,
ICPR21(10383-10389)
IEEE DOI
2105
Training, Protocols, Image reconstruction
BibRef
Chang, X.Y.[Xin-Yuan],
Tao, X.Y.[Xiao-Yu],
Hong, X.P.[Xiao-Peng],
Wei, X.[Xing],
Ke, W.[Wei],
Gong, Y.H.[Yi-Hong],
Class-Incremental Learning with Topological Schemas of Memory Spaces,
ICPR21(9719-9726)
IEEE DOI
2105
Multiprotocol label switching, Manifolds, Knowledge engineering,
Adaptation models, Network topology, Neural networks,
Topological Schemas Model
BibRef
Pernici, F.[Federico],
Bruni, M.[Matteo],
Baecchi, C.[Claudio],
Turchini, F.[Francesco],
del Bimbo, A.[Alberto],
Class-incremental Learning with Pre-allocated Fixed Classifiers,
ICPR21(6259-6266)
IEEE DOI
2105
Training, Knowledge engineering, Neural networks, Standards, Faces
BibRef
Buzzega, P.[Pietro],
Boschini, M.[Matteo],
Porrello, A.[Angelo],
Calderara, S.[Simone],
Rethinking Experience Replay: a Bag of Tricks for Continual Learning,
ICPR21(2180-2187)
IEEE DOI
2105
Degradation, Neural networks, Proposals, Erbium, Standards
BibRef
Fidalgo-Merino, R.[Raúl],
Gabrielli, L.[Lorenzo],
Checchi, E.[Enrico],
Leveraging Sequential Pattern Information for Active Learning from
Sequential Data,
ICPR21(6957-6964)
IEEE DOI
2105
Training, Machine learning algorithms, Annotations, Databases,
Manuals, Machine learning, Data models
BibRef
Agarwal, A.[Arvind],
Mujumdar, S.[Shashank],
Gupta, N.[Nitin],
Mehta, S.[Sameep],
Budgeted Batch Mode Active Learning with Generalized Cost and Utility
Functions,
ICPR21(7692-7698)
IEEE DOI
2105
Learning systems, Training data, Cost function, Data models,
Labeling
BibRef
Herde, M.[Marek],
Kottke, D.[Daniel],
Huseljic, D.[Denis],
Sick, B.[Bernhard],
Multi-Annotator Probabilistic Active Learning,
ICPR21(10281-10288)
IEEE DOI
2105
Training, Deep learning, Annotations, Computational modeling,
Employment, Manuals, Gaussian processes
BibRef
Arnavaz, K.[Kasra],
Feragen, A.[Aasa],
Krause, O.[Oswin],
Loog, M.[Marco],
Bayesian Active Learning for Maximal Information Gain on Model
Parameters,
ICPR21(10524-10531)
IEEE DOI
2105
Machine learning, Data models, Bayes methods, Logistics
BibRef
Li, M.H.[Ming-Han],
Liu, X.L.[Xia-Lei],
van de Weijer, J.[Joost],
Raducanu, B.[Bogdan],
Learning to Rank for Active Learning: A Listwise Approach,
ICPR21(5587-5594)
IEEE DOI
2105
Training, Measurement, Correlation, Prediction algorithms,
Classification algorithms, Labeling
BibRef
Siméoni, O.[Oriane],
Budnik, M.[Mateusz],
Avrithis, Y.[Yannis],
Gravier, G.[Guillaume],
Rethinking deep active learning:
Using unlabeled data at model training,
ICPR21(1220-1227)
IEEE DOI
2105
Training, Deep learning, Pipelines, Semisupervised learning,
Data models, Image classification
BibRef
Li, C.[Cheng],
Rana, S.[Santu],
Gill, A.[Andrew],
Nguyen, D.[Dang],
Gupta, S.I.[Sun-Il],
Venkatesh, S.[Svetha],
Factor Screening using Bayesian Active Learning and Gaussian Process
Meta-Modelling,
ICPR21(3288-3295)
IEEE DOI
2105
Gaussian processes, Length measurement, Entropy, Bayes methods,
Kernel, Factor screening, Gaussian Process
BibRef
Li, X.O.[Xia-Obin],
Shan, L.[Lianlei],
Li, M.[Minglong],
Wang, W.Q.[Wei-Qiang],
Energy Minimum Regularization in Continual Learning,
ICPR21(6404-6409)
IEEE DOI
2105
Learning systems, Sensitivity, Animals, Solids, Minimization,
Pattern recognition, Task analysis
BibRef
Ho, C.H.[Chih-Hsing],
Tsai, S.H.L.[Shang-Ho Lawrence],
RSAC: Regularized Subspace Approximation Classifier for Lightweight
Continuous Learning,
ICPR21(6680-6687)
IEEE DOI
2105
Training, Memory management, Training data,
Approximation algorithms, Classification algorithms,
Streaming Learning
BibRef
Lei, C.H.[Cheng-Hsun],
Chen, Y.H.[Yi-Hsin],
Peng, W.H.[Wen-Hsiao],
Chiu, W.C.[Wei-Chen],
Class-Incremental Learning with Rectified Feature-Graph Preservation,
ACCV20(VI:358-374).
Springer DOI
2103
Learn new classes as they arrive.
BibRef
Chan, D.M.[David M.],
Vijayanarasimhan, S.[Sudheendra],
Ross, D.A.[David A.],
Canny, J.F.[John F.],
Active Learning for Video Description with Cluster-regularized Ensemble
Ranking,
ACCV20(V:443-459).
Springer DOI
2103
BibRef
Wang, S.[Shuo],
Li, Y.X.[Yue-Xiang],
Ma, K.[Kai],
Ma, R.[Ruhui],
Guan, H.B.[Hai-Bing],
Zheng, Y.F.[Ye-Feng],
Dual Adversarial Network for Deep Active Learning,
ECCV20(XXIV:680-696).
Springer DOI
2012
BibRef
Lin, Z.[Zudi],
Wei, D.L.[Dong-Lai],
Jang, W.D.[Won-Dong],
Zhou, S.[Siyan],
Chen, X.P.[Xu-Peng],
Wang, X.Y.[Xue-Ying],
Schalek, R.[Richard],
Berger, D.[Daniel],
Matejek, B.[Brian],
Kamentsky, L.[Lee],
Peleg, A.[Adi],
Haehn, D.[Daniel],
Jones, T.[Thouis],
Parag, T.[Toufiq],
Lichtman, J.[Jeff],
Pfister, H.[Hanspeter],
Two Stream Active Query Suggestion for Active Learning in Connectomics,
ECCV20(XVIII:103-120).
Springer DOI
2012
BibRef
Ebrahimi, S.[Sayna],
Meier, F.[Franziska],
Calandra, R.[Roberto],
Darrell, T.J.[Trevor J.],
Rohrbach, M.[Marcus],
Adversarial Continual Learning,
ECCV20(XI:386-402).
Springer DOI
2011
BibRef
Kim, C.D.[Chris Dongjoo],
Jeong, J.[Jinseo],
Kim, G.[Gunhee],
Imbalanced Continual Learning with Partitioning Reservoir Sampling,
ECCV20(XIII:411-428).
Springer DOI
2011
BibRef
Kim, E.S.,
Kim, J.U.,
Lee, S.,
Moon, S.K.,
Ro, Y.M.,
Class Incremental Learning With Task-Selection,
ICIP20(1846-1850)
IEEE DOI
2011
Task analysis, Learning systems, Image reconstruction,
Feature extraction, Training, Testing, Data models, Deep learning, autoencoder
BibRef
Gao, M.F.[Ming-Fei],
Zhang, Z.Z.[Zi-Zhao],
Yu, G.[Guo],
Arik, S.Ö.[Sercan Ö.],
Davis, L.S.[Larry S.],
Pfister, T.[Tomas],
Consistency-based Semi-supervised Active Learning:
Towards Minimizing Labeling Cost,
ECCV20(X:510-526).
Springer DOI
2011
BibRef
Chaplot, D.S.[Devendra Singh],
Jiang, H.[Helen],
Gupta, S.[Saurabh],
Gupta, A.[Abhinav],
Semantic Curiosity for Active Visual Learning,
ECCV20(VI:309-326).
Springer DOI
2011
BibRef
Fini, E.[Enrico],
Lathuilière, S.[Stéphane],
Sangineto, E.[Enver],
Nabi, M.[Moin],
Ricci, E.[Elisa],
Online Continual Learning Under Extreme Memory Constraints,
ECCV20(XXVIII:720-735).
Springer DOI
2011
BibRef
Agarwal, S.[Sharat],
Arora, H.[Himanshu],
Anand, S.[Saket],
Arora, C.[Chetan],
Contextual Diversity for Active Learning,
ECCV20(XVI: 137-153).
Springer DOI
2010
BibRef
Iscen, A.[Ahmet],
Zhang, J.[Jeffrey],
Lazebnik, S.[Svetlana],
Schmid, C.[Cordelia],
Memory-efficient Incremental Learning Through Feature Adaptation,
ECCV20(XVI: 699-715).
Springer DOI
2010
BibRef
Yu, L.,
Twardowski, B.,
Liu, X.,
Herranz, L.,
Wang, K.,
Cheng, Y.,
Jui, S.,
van de Weijer, J.,
Semantic Drift Compensation for Class-Incremental Learning,
CVPR20(6980-6989)
IEEE DOI
2008
Task analysis, Training, Prototypes, Semantics, Measurement, Neurons
BibRef
Zhao, B.,
Xiao, X.,
Gan, G.,
Zhang, B.,
Xia, S.,
Maintaining Discrimination and Fairness in Class Incremental Learning,
CVPR20(13205-13214)
IEEE DOI
2008
Training, Task analysis, Data models, Error analysis,
Neural networks, Standards
BibRef
Rajasegaran, J.,
Khan, S.,
Hayat, M.,
Khan, F.S.,
Shah, M.,
iTAML: An Incremental Task-Agnostic Meta-learning Approach,
CVPR20(13585-13594)
IEEE DOI
2008
Task analysis, Adaptation models, Training, Stability analysis,
Interference, Predictive models, Heuristic algorithms
BibRef
He, J.,
Mao, R.,
Shao, Z.,
Zhu, F.,
Incremental Learning in Online Scenario,
CVPR20(13923-13932)
IEEE DOI
2008
Data models, Machine learning, Training, Task analysis,
Feature extraction, Predictive models, Learning systems
BibRef
Mi, F.,
Kong, L.,
Lin, T.,
Yu, K.,
Faltings, B.,
Generalized Class Incremental Learning,
CLVision20(970-974)
IEEE DOI
2008
Erbium, Training, Data models, Computational modeling,
Probabilistic logic, Machine learning, Task analysis
BibRef
Ayub, A.,
Wagner, A.R.,
Cognitively-Inspired Model for Incremental Learning Using a Few
Examples,
CLVision20(897-906)
IEEE DOI
2008
Feature extraction, Task analysis, Training, Machine learning,
Training data, Data models, Hippocampus
BibRef
Hayes, T.L.,
Kanan, C.,
Lifelong Machine Learning with Deep Streaming Linear Discriminant
Analysis,
CLVision20(887-896)
IEEE DOI
2008
Streaming media, Covariance matrices, Training,
Computational modeling, Neural networks, Task analysis,
Linear discriminant analysis
BibRef
Dhar, P.[Prithviraj],
Singh, R.V.[Rajat Vikram],
Peng, K.C.[Kuan-Chuan],
Wu, Z.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
Prabhu, A.[Ameya],
Torr, P.H.S.[Philip H. S.],
Dokania, P.K.[Puneet K.],
GDUMB:
A Simple Approach that Questions Our Progress in Continual Learning,
ECCV20(II:524-540).
Springer DOI
2011
BibRef
Chaudhry, A.[Arslan],
Dokania, P.K.[Puneet K.],
Ajanthan, T.[Thalaiyasingam],
Torr, P.H.S.[Philip H. S.],
Riemannian Walk for Incremental Learning:
Understanding Forgetting and Intransigence,
ECCV18(XI: 556-572).
Springer DOI
1810
BibRef
Lomonaco, V.,
Maltoni, D.,
Pellegrini, L.,
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches,
CLVision20(989-998)
IEEE DOI
2008
Training, Robots, Videos, Object recognition, Benchmark testing,
Computational modeling
BibRef
Silver, D.L.,
Mahfuz, S.,
Generating Accurate Pseudo Examples for Continual Learning,
CLVision20(1035-1042)
IEEE DOI
2008
Task analysis, Training, Probability distribution,
Knowledge engineering, Input variables,
Neural networks
BibRef
Parshotam, K.,
Kilickaya, M.,
Continual Learning of Object Instances,
CLVision20(907-914)
IEEE DOI
2008
Automobiles, Task analysis, Measurement, Training, Data models,
Visualization, Companies
BibRef
Liu, X.,
Wu, C.,
Menta, M.,
Herranz, L.,
Raducanu, B.,
Bagdanov, A.D.,
Jui, S.,
van de Weijer, J.,
Generative Feature Replay For Class-Incremental Learning,
CLVision20(915-924)
IEEE DOI
2008
Task analysis, Feature extraction,
Image generation, Correlation, Training, Generators
BibRef
Mirzadeh, S.I.,
Farajtabar, M.,
Ghasemzadeh, H.,
Dropout as an Implicit Gating Mechanism For Continual Learning,
CLVision20(945-951)
IEEE DOI
2008
Task analysis, Neurons, Stability analysis, Training, Standards,
Logic gates, Knowledge engineering
BibRef
Liu, Y.,
Su, Y.,
Liu, A.,
Schiele, B.,
Sun, Q.,
Mnemonics Training: Multi-Class Incremental Learning Without
Forgetting,
CVPR20(12242-12251)
IEEE DOI
2008
Training, Optimization, Data models, Computational modeling,
Generative adversarial networks, Training data
BibRef
Zhang, J.[Jie],
Zhang, J.T.[Jun-Ting],
Ghosh, S.[Shalini],
Li, D.W.[Da-Wei],
Zhu, J.W.[Jing-Wen],
Zhang, H.M.[He-Ming],
Wang, Y.L.[Ya-Lin],
Regularize, Expand and Compress: NonExpansive Continual Learning,
WACV20(843-851)
IEEE DOI
2006
Task analysis, Computational modeling,
Network architecture, Neural networks, Knowledge engineering, Correlation
BibRef
Slim, H.[Habib],
Belouadah, E.[Eden],
Popescu, A.[Adrian],
Onchis, D.[Darian],
Dataset Knowledge Transfer for Class-Incremental Learning without
Memory,
WACV22(3311-3320)
IEEE DOI
2202
Training, Deep learning, Design methodology,
Memory management, Neural networks, Semi- and Un- supervised Learning
BibRef
Belouadah, E.[Eden],
Popescu, A.[Adrian],
ScaIL: Classifier Weights Scaling for Class Incremental Learning,
WACV20(1255-1264)
IEEE DOI
2006
BibRef
Earlier:
IL2M: Class Incremental Learning With Dual Memory,
ICCV19(583-592)
IEEE DOI
2004
Tuning, Adaptation models, Training, Feature extraction,
Machine learning, Memory management, Task analysis.
computational complexity, image classification,
inference mechanisms, learning (artificial intelligence),
Computer architecture
BibRef
Ostapenko, O.[Oleksiy],
Puscas, M.[Mihai],
Klein, T.[Tassilo],
Jahnichen, P.[Patrick],
Nabi, M.[Moin],
Learning to Remember: A Synaptic Plasticity Driven Framework for
Continual Learning,
CVPR19(11313-11321).
IEEE DOI
2002
BibRef
Stojanov, S.[Stefan],
Mishra, S.[Samarth],
Thai, N.A.[Ngoc Anh],
Dhanda, N.[Nikhil],
Humayun, A.[Ahmad],
Yu, C.[Chen],
Smith, L.B.[Linda B.],
Rehg, J.M.[James M.],
Incremental Object Learning From Contiguous Views,
CVPR19(8769-8778).
IEEE DOI
2002
BibRef
Murata, K.[Kengo],
Toyota, T.[Tetsuya],
Ohara, K.[Kouzou],
What is Happening Inside a Continual Learning Model?:
A Representation-Based Evaluation of Representational Forgetting,
CLVision20(952-956)
IEEE DOI
2008
Task analysis, Erbium, Measurement, Learning systems, Standards,
Neural networks, Data models
BibRef
Abati, D.,
Tomczak, J.,
Blankevoort, T.,
Calderara, S.,
Cucchiara, R.,
Bejnordi, B.E.,
Conditional Channel Gated Networks for Task-Aware Continual Learning,
CVPR20(3930-3939)
IEEE DOI
2008
Task analysis, Logic gates, Training, Computational modeling,
Neural networks, Machine learning, Computer architecture
BibRef
Lee, J.,
Hong, H.G.,
Joo, D.,
Kim, J.,
Continual Learning With Extended Kronecker-Factored Approximate
Curvature,
CVPR20(8998-9007)
IEEE DOI
2008
Task analysis, Neural networks, Mathematical model,
Learning systems, Optimization, Network architecture, Training
BibRef
Kim, J.,
Kim, J.,
Kwak, N.,
StackNet: Stacking feature maps for Continual learning,
CLVision20(975-982)
IEEE DOI
2008
Task analysis, Indexes, Training, Data models,
Biological neural networks, Stacking, Machine learning
BibRef
Du, X.,
Li, Z.,
Seo, J.,
Liu, F.,
Cao, Y.,
Noise-based Selection of Robust Inherited Model for Accurate
Continual Learning,
CLVision20(983-988)
IEEE DOI
2008
Pattern recognition
BibRef
Lomonaco, V.,
Desai, K.,
Culurciello, E.,
Maltoni, D.,
Continual Reinforcement Learning in 3D Non-stationary Environments,
CLVision20(999-1008)
IEEE DOI
2008
Task analysis, Learning (artificial intelligence),
Benchmark testing, Color, Training, Complexity theory
BibRef
Aljundi, R.[Rahaf],
Kelchtermans, K.[Klaas],
Tuytelaars, T.[Tinne],
Task-Free Continual Learning,
CVPR19(11246-11255).
IEEE DOI
2002
BibRef
Park, D.M.[Dong-Min],
Hong, S.[Seokil],
Han, B.H.[Bo-Hyung],
Lee, K.M.[Kyoung Mu],
Continual Learning by Asymmetric Loss Approximation With Single-Side
Overestimation,
ICCV19(3334-3343)
IEEE DOI
2004
function approximation, learning (artificial intelligence),
neural nets, asymmetric loss approximation, Scalability
BibRef
El Khatib, A.[Alaa],
Karray, F.[Fakhri],
Strategies for Improving Single-Head Continual Learning Performance,
ICIAR19(I:452-460).
Springer DOI
1909
Forgetting. Problem is also not all data is available at once.
BibRef
Hayes, T.L.,
Kemker, R.,
Cahill, N.D.,
Kanan, C.,
New Metrics and Experimental Paradigms for Continual Learning,
DeepLearnRV18(2112-21123)
IEEE DOI
1812
Robots, Measurement, Training, Task analysis, Computational modeling,
Neural networks, Data models
BibRef
Zhai, M.Y.[Meng-Yao],
Chen, L.[Lei],
Mori, G.[Greg],
Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned
Generation,
CVPR21(2246-2255)
IEEE DOI
2111
Deep learning, Costs, Heuristic algorithms, Memory management,
Filtering algorithms, Information filters, Generators
BibRef
Zhai, M.Y.[Meng-Yao],
Chen, L.[Lei],
He, J.W.[Jia-Wei],
Nawhal, M.[Megha],
Tung, F.[Frederick],
Mori, G.[Greg],
Piggyback GAN:
Efficient Lifelong Learning for Image Conditioned Generation,
ECCV20(XXI:397-413).
Springer DOI
2011
BibRef
Earlier: A1, A2, A5, A3, A4, A6:
Lifelong GAN: Continual Learning for Conditional Image Generation,
ICCV19(2759-2768)
IEEE DOI
2004
image processing, learning (artificial intelligence),
neural nets, continual learning, deep neural networks, Training data
BibRef
Lopes, N.[Noel],
Ribeiro, B.[Bernardete],
Trading off Distance Metrics vs Accuracy in Incremental Learning
Algorithms,
CIARP16(530-538).
Springer DOI
1703
BibRef
Earlier:
On the Impact of Distance Metrics in Instance-Based Learning Algorithms,
IbPRIA15(48-56).
Springer DOI
1506
BibRef
Ditzler, G.[Gregory],
Polikar, R.[Robi],
Chawla, N.V.[Nitesh V.],
An Incremental Learning Algorithm for Non-stationary Environments and
Class Imbalance,
ICPR10(2997-3000).
IEEE DOI
1008
BibRef
Almaksour, A.[Abdullah],
Anquetil, E.[Eric],
Quiniou, S.[Solen],
Cheriet, M.[Mohamed],
Evolving Fuzzy Classifiers: Application to Incremental Learning of
Handwritten Gesture Recognition Systems,
ICPR10(4056-4059).
IEEE DOI
1008
BibRef
Sudo, K.[Kyoko],
Osawa, T.[Tatsuya],
Tanaka, H.[Hidenori],
Koike, H.[Hideki],
Arakawa, K.[Kenichi],
Online anomal movement detection based on unsupervised incremental
learning,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Zhang, R.[Rong],
Rudnicky, A.I.[Alexander I.],
A New Data Selection Principle for Semi-Supervised Incremental Learning,
ICPR06(II: 780-783).
IEEE DOI
0609
BibRef
Prehn, H.[Herward],
Sommer, G.[Gerald],
An Adaptive Classification Algorithm Using Robust Incremental
Clustering,
ICPR06(I: 896-899).
IEEE DOI
0609
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Subspace Clustering, Subspace Learning .