14.2.6.1.1 Active Learning

Chapter Contents (Back)
Active Learning.

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

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

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

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

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

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

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

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

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

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

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

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

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

Shoham, N.[Neta], Avron, H.[Haim],
Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning,
PAMI(45), No. 10, October 2023, pp. 11766-11777.
IEEE DOI 2310
BibRef

Wang, M.[Min], Wen, T.[Ting], Jiang, X.Y.[Xiao-Yu], Zhang, A.A.[An-An],
Open set transfer learning through distribution driven active learning,
PR(146), 2024, pp. 110055.
Elsevier DOI 2311
Active learning, Transfer learning, Evidence learning, Uncertainty analysis BibRef


Yu, F.G.[Feng-Gen], Qian, Y.M.[Yi-Ming], Gil-Ureta, F.[Francisca], Jackson, B.[Brian], Bennett, E.[Eric], Zhang, H.[Hao],
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling,
ICCV23(865-875)
IEEE DOI 2401
BibRef

Wang, Y.T.[Yu-Ting], Ilic, V.[Velibor], Li, J.[Jiatong], Kisacanin, B.[Branislav], Pavlovic, V.[Vladimir],
ALWOD: Active Learning for Weakly-Supervised Object Detection,
ICCV23(6436-6446)
IEEE DOI Code:
WWW Link. 2401
BibRef

Kye, S.M.[Seong Min], Choi, K.[Kwanghee], Byun, H.[Hyeongmin], Chang, B.[Buru],
TiDAL: Learning Training Dynamics for Active Learning,
ICCV23(22278-22288)
IEEE DOI 2401
BibRef

Hekimoglu, A.[Aral], Friedrich, P.[Philipp], Zimmer, W.[Walter], Schmidt, M.[Michael], Marcos-Ramiro, A.[Alvaro], Knoll, A.[Alois],
Multi-Task Consistency for Active Learning,
VCL23(3407-3416)
IEEE DOI Code:
WWW Link. 2401
BibRef

Wu, T.H.[Tsung-Han], Su, H.T.[Hung-Ting], Chen, S.T.[Shang-Tse], Hsu, W.H.[Winston H.],
Fair Robust Active Learning by Joint Inconsistency,
AROW23(3624-3633)
IEEE DOI 2401
BibRef

Cruz, R.P.M.[Ricardo P. M.], Shihavuddin, A.S.M., Maruf, M.H.[Md. Hasan], Cardoso, J.S.[Jaime S.],
Active Supervision: Human in the Loop,
CIARP23(I:540-551).
Springer DOI 2312
BibRef

Li, J.N.[Jia-Ning], Du, Y.[Yuan], Du, L.[Li],
Siamese Network Representation for Active Learning,
ICIP23(131-135)
IEEE DOI 2312
BibRef

Su, G.L.[Guo-Liang], Wu, Z.Q.[Zhang-Quan], Ye, Y.[Yujia], Chen, M.[Maoxing], Zhou, J.[Jun],
Cost-Efficient Multi-Instance Multi-Label Active Learning Via Correlation of Features,
ICIP23(410-414)
IEEE DOI 2312
BibRef

Ye, Y.[Yujia], Wu, Z.Q.[Zhang-Quan], Su, G.L.[Guo-Liang], Zhou, J.[Jun],
Task-Aware Graph Convolutional Network for Active Learning,
ICIP23(495-499)
IEEE DOI 2312
BibRef

Liu, Y.[Ying], Pang, Y.L.[Yu-Liang], Zhang, W.D.[Wei-Dong],
Deep Active Learning Based on Saliency-Guided Data Augmentation for Image Classification,
ICIP23(815-819)
IEEE DOI 2312
BibRef

Rana, A.J.[Aayush J], Rawat, Y.S.[Yogesh S],
Hybrid Active Learning via Deep Clustering for Video Action Detection,
CVPR23(18867-18877)
IEEE DOI 2309
BibRef

Ji, W.[Wei], Liang, R.J.[Ren-Jie], Zheng, Z.[Zhedong], Zhang, W.Q.[Wen-Qiao], Zhang, S.Y.[Sheng-Yu], Li, J.C.[Jun-Cheng], Li, M.Z.[Meng-Ze], Chua, T.S.[Tat-Seng],
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-based Active Learning,
CVPR23(23013-23022)
IEEE DOI 2309
BibRef

Kim, S.M.[Sang-Mook], Bae, S.[Sangmin], Song, H.[Hwanjun], Yun, S.Y.[Se-Young],
Re-Thinking Federated Active Learning Based on Inter-Class Diversity,
CVPR23(3944-3953)
IEEE DOI 2309
BibRef

Mohamadi, S.[Salman], Doretto, G.[Gianfranco], Adjeroh, D.A.[Donald A.],
Deep Active Ensemble Sampling for Image Classification,
ACCV22(VII:713-729).
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


Last update:Jan 30, 2024 at 20:33:16