14.2.6.1.2 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.W.[Shu-Wen], 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

Li, W.W.[Wei-Wei], Qian, W.[Wei], Chen, L.[Lei], Jia, X.[Xiuyi],
Sample diversity selection strategy based on label distribution morphology for active label distribution learning,
PR(150), 2024, pp. 110322.
Elsevier DOI 2403
Label distribution learning, Active learning, Representativeness, Diversity, Label distribution morphology BibRef

Tan, W.[Wei], Du, L.[Lan], Buntine, W.[Wray],
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning,
PAMI(46), No. 5, May 2024, pp. 3463-3479.
IEEE DOI 2404
Uncertainty, Costs, Bayes methods, Computational modeling, Task analysis, Convergence, Predictive models, Active learning, text classification BibRef

Han, Y.C.[Yin-Cheng], Liu, D.J.[Da-Jiang], Shang, J.X.[Jia-Xing], Zheng, L.[Linjiang], Zhong, J.[Jiang], Cao, W.W.[Wei-Wei], Sun, H.[Hong], Xie, W.[Wu],
BALQUE: Batch active learning by querying unstable examples with calibrated confidence,
PR(151), 2024, pp. 110385.
Elsevier DOI 2404
Active learning, Confidence calibration, Deep neural networks, Machine learning, Image classification BibRef

Hu, Q.H.[Qing-Hua], Ji, L.[Luona], Wang, Y.[Yu], Zhao, S.[Shuai], Lin, Z.B.[Zhi-Bin],
Uncertainty-driven active developmental learning,
PR(151), 2024, pp. 110384.
Elsevier DOI 2404
Object detection, Active developmental learning, Uncertainty estimation BibRef

Yang, J.C.[Jia-Chen], Ma, S.[Shukun], Zhang, Z.[Zhuo], Li, Y.[Yang], Xiao, S.[Shuai], Wen, J.[JiaBao], Lu, W.[Wen], Gao, X.B.[Xin-Bo],
Say No to Redundant Information: Unsupervised Redundant Feature Elimination for Active Learning,
MultMed(26), 2024, pp. 7721-7733.
IEEE DOI 2405
Task analysis, Data models, Labeling, Costs, Training, Redundancy, Computational modeling, Active learning, data issues, label noise BibRef


Duan, R.X.[Ru-Xiao], Caffo, B.[Brian], Bai, H.X.[Harrison X.], Sair, H.I.[Haris I.], Jones, C.[Craig],
Evidential Uncertainty Quantification: A Variance-Based Perspective,
WACV24(2121-2130)
IEEE DOI Code:
WWW Link. 2404
Deep learning, Adaptation models, Uncertainty, Correlation, Codes, Artificial neural networks, Algorithms, Image recognition and understanding BibRef

Kanebako, Y.[Yusuke],
Critical Gap Between Generalization Error and Empirical Error in Active Learning,
WACV24(2759-2767)
IEEE DOI 2404
Training, Uncertainty, Costs, Annotations, Production, Data models, Algorithms, Machine learning architectures, formulations, Image recognition and understanding BibRef

Beck, N.[Nathan], Killamsetty, K.[Krishnateja], Kothawade, S.[Suraj], Iyer, R.[Rishabh],
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification,
WACV24(2869-2877)
IEEE DOI 2404
Adaptation models, Costs, Uncertainty, Human in the loop, Data models, Labeling, Algorithms, Machine learning architectures, Image recognition and understanding BibRef

Stojnic, V.[Vladan], Laskar, Z.[Zakaria], Tolias, G.[Giorgos],
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning,
WACV24(259-268)
IEEE DOI Code:
WWW Link. 2404
Training, Codes, Annotations, Source coding, Data acquisition, Semisupervised learning, Algorithms 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], Schmidt, M.[Michael], Marcos-Ramiro, A.[Alvaro],
Active Learning with Task Consistency and Diversity in Multi-Task Networks,
WACV24(2491-2500)
IEEE DOI Code:
WWW Link. 2404
Training, Codes, Annotations, Semisupervised learning, Multitasking, Feature extraction, Algorithms, Machine learning architectures, Image recognition and understanding 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

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:May 6, 2024 at 15:50:14