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
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 .