The Power of Ensembles for Active Learning in Image Classification

Cited by (654)

Items 201–300 of 654

1. Kecor: Kernel Coding Rate Maximization for Active 3D Object Detection
   Yadan Luo, Zhuoxiao Chen, Zhen Fang, Zheng Zhang, Mahsa Baktashmotlagh, Zi Helen Huang · 2023 · IEEE/CVF International Conference on Computer Vision (ICCV)
2. Uncertainty based active learning with deep neural networks for inertial gait analysis
   Alexander Vaith, Bertram Taetz, Gabriele Bleser · 2020 · International Conference on Information Fusion (FUSION)
3. A Mathematical Analysis of Learning Loss for Active Learning in Regression
   Megh Shukla, Shuaib Ahmed · 2021 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
4. Active dropblock: Method to enhance deep model accuracy and robustness
   Jie Yao, Weiwei Xing, Dongdong Wang, Jintao Xing, Liqiang Wang · 2021 · Neurocomputing
5. How do People Train a Machine?
   Téo Sanchez, Baptiste Caramiaux, Jules Françoise, Frederic P. Bevilacqua, Wendy E. Mackay · 2021 · Proceedings of the ACM on Human-Computer Interaction
6. Active Learning for Text Classification and Fake News Detection
   Marko Sahan, Václav Šmídl, Radek Mařík · 2021 · International Symposium on Computer Science and Intelligent Control (ISCSIC)
7. A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
   I. Laradji, Pau Rodríguez López, Frederic Branchaud-Charron, Keegan Lensink, Parmida Atighehchian, William Parker, David Vázquez, Derek Nowrouzezahrai · 2020 · arXiv.org
8. A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science
   Jinghou Bi, Yuanhao Xu, Felix Conrad, Hajo Wiemer, Steffen Ihlenfeldt · 2025 · Scientific Reports
9. Uncertainty Assessment-Based Active Learning for Reliable Fire Detection Systems
   Young-Jin Kim, Won-Tae Kim · 2022 · IEEE Access
10. Exploring Active Learning for Semiconductor Defect Segmentation
    Lile Cai, Ramanpreet Singh Pahwa, Xun Xu, Jie Wang, Richard Chang, Lining Zhang, Chuan-Sheng Foo · 2022 · Proceedings - International Conference on Image Processing
11. Image super-resolution inspired electron density prediction
    Chenghan Li, Or Sharir, Shunyue Yuan, Garnet Kin‐Lic Chan · 2025 · Nature Communications
12. Active Learning of Abstract Plan Feasibility
    Michael Noseworthy, C. Moses, Isa Brand, Sebastian Castro, L. Kaelbling, Tomas Lozano-Perez, N. Roy · 2021 · Robotics: Science and Systems Conference
13. Exploring Spatial Diversity for Region-Based Active Learning
    Li-Le Cai, Xun Xu, Li-Ning Zhang, Chuan-Sheng Foo · 2021 · IEEE Transactions on Image Processing
14. Interpretable Feedback for AutoML and a Proposal for Domain-customized AutoML for Networking
    Behnaz Arzani, Kevin Hsieh, Haoxian Chen · 2021 · ACM Workshop on Hot Topics in Networks (HotNets)
15. Human-Machine Hybrid Peer Grading in SPOCs
    Yong Han, Wenjun Wu, Yitao Yan, Lijun Zhang · 2020 · IEEE Access
16. Quality-driven deep active learning method for 3D brain MRI segmentation
    Zhenxi Zhang, Jie Li, Chun-Na Tian, Zhu-Si Zhong, Z. Jiao, Xin-Bo Gao · 2021 · Neurocomputing
17. Active Pointly-Supervised Instance Segmentation
    Chufeng Tang, Lingxi Xie, Gang Zhang, Xiaopeng Zhang, Qi Chuan Tian, Xiaolin Hu · 2022 · Lecture notes in computer science
18. Gaussian Switch Sampling: A Second-Order Approach to Active Learning
    Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib, Armin Pacharmi, Enrique Corona · 2023 · IEEE Transactions on Artificial Intelligence
19. Multi-resolution consistency semi-supervised active learning framework for histopathology image classification
    Mingjian Xie, Yiqun Geng, Weifeng Zhang, Shan Cang Li, Yuejiao Dong, Yongjun Wu, Hongzhong Tang, Liangli Hong · 2024 · Expert Systems with Applications
20. Accelerating materials discovery through active learning: Methods, challenges and opportunities
    Yujie Ma, Yang Gao, Ludi Wang, Ming Chen, Wenjuan Cui, Bin Wang, Yi Du · 2025 · The Innovation Informatics
21. Parallel Multistage Wide Neural Network
    Jiangbo Xi, Okan K. Ersoy, Jianwu Fang, Tianjun Wu, Xin Wei, Chaoying Zhao · 2021 · IEEE Transactions on Neural Networks and Learning Systems
22. Active Class Incremental Learning for Imbalanced Datasets
    Eden Belouadah, Adrian Popescu, Umang Aggarwal, Léo Saci · 2020 · Lecture notes in computer science
23. Diverse Complexity Measures for Dataset Curation in Self-Driving
    Abbas Sadat, Sean Segal, Sergio Casas, James Tu, Bin Yang, Raquel Urtasun, Ersin Yumer · 2021 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
24. Efficacy of Bayesian Neural Networks in Active Learning
    Vineeth Rakesh, Swayambhoo Jain · 2021 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
25. Human+AI Crowd Task Assignment Considering Result Quality Requirements
    Masaki Kobayashi, Kei Wakabayashi, Atsuyuki Morishima · 2021 · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
26. Deep Active Learning via Open-Set Recognition
    Jaya Krishna Mandivarapu, Blake Camp, Rolando G. Estrada · 2022 · Frontiers in Artificial Intelligence
27. Dual generative adversarial active learning
    Jifeng Guo, Zhiqi Pang, Miaoyuan Bai, Peijiao Xie, Yu Chen · 2021 · Applied Intelligence
28. Active Learning Based 3D Semantic Labeling From Images and Videos
    Mengqi Rong, Hainan Cui, Zhanyi Hu, Hanqing Jiang, Hongmin Liu, Shuhan Shen · 2021 · IEEE Transactions on Circuits and Systems for Video Technology
29. Optical Flow Training Under Limited Label Budget via Active Learning
    Shuai Yuan, Xian Sun, Hannah Halin Kim, Shuzhi Yu, Carlo Tomasi · 2022 · Lecture notes in computer science
30. DivTheft: An Ensemble Model Stealing Attack by Divide-and-Conquer
    Zhuo Ma, Xinjing Liu, Yang Liu, Ximeng Liu, Zhan Qin, Kui Ren · 2023 · IEEE Transactions on Dependable and Secure Computing
31. Stealing and evading malware classifiers and antivirus at low false positive conditions
    María Rigaki, Sebastián García · 2023 · Computers & Security
32. Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking
    Rui Li, Baopeng Zhang, Jun Liu, Wei Liu, Jian Cun Zhao, Zhu Teng · 2023 · IEEE/CVF International Conference on Computer Vision (ICCV)
33. Say No to Redundant Information: Unsupervised Redundant Feature Elimination for Active Learning
    Jia-Chen Yang, Shu-Kun Ma, Zhuo Zhang, Yang Li, Shuai Xiao, Jia-Bao Wen, Wen Lu, Xin-Bo Gao · 2024 · IEEE transactions on multimedia
34. Exquisitor
    Hanna Ragnarsdóttir, Þórhildur Þorleiksdóttir, Omar Shahbaz Khan, Björn Þór Jónsson, Gylfi Þór Guðmundsson, Jan Zahálka, Stevan Rudinac, Laurent Amsaleg, Marcel Worring · 2019 · ACM International Conference on Multimedia (ACM MM)
35. Weight Decay Scheduling and Knowledge Distillation for Active Learning
    Juseung Yun, Byung-Joo Kim, Junmo Kim · 2020 · Lecture notes in computer science
36. Uncertainty-Aware Data Augmentation for Food Recognition
    Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khantun, Marc Bolaños, Petia I. Radeva · 2021 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition
37. Improve Unsupervised Pretraining for Few-label Transfer
    Suichan Li, Dongdong Chen, Yinpeng Chen, Lu Yuan, Lei Zhang, Qi Chu, Bin Liu, Nenghai Yu · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
38. Deep active learning with Weighting filter for object detection
    Huang, Wei, Sun, Shuzhou, Lin, Xiao, Zhang, Dawei, Ma, Lizhuang · 2023 · Displays
39. Detecting Concept Drift with Neural Network Model Uncertainty
    Lucas Baier, Tim Schlör, Jakob Schoeffer, Niklas Kühl · 2023 · Hawaii International Conference on System Sciences (HICSS)
40. Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
    Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, Muhao Chen · 2023 · Conference of the European Chapter of the Association for Computational Linguistics
41. Managing the unknown in machine learning: Definitions, related areas, recent advances, and prospects
    Marcos Barcina-Blanco, Jesús L. Lobo, Pablo G. Bringas, Javier Del Ser · 2024 · Neurocomputing
42. Federated Active Learning Framework for Efficient Annotation Strategy in Skin-Lesion Classification
    Zhipeng Deng, Y.S. Yang, Kenji Suzuki · 2024 · Journal of Investigative Dermatology
43. Efficient Active Learning Strategies for Monocular 3D Object Detection
    Aral Hekimoglu, Michael Schmidt, Alvaro Marcos-Ramiro, Gerhard Rigoll · 2022 · IEEE Intelligent Vehicles Symposium
44. Image Information Contribution Evaluation for Plant Diseases Classification via Inter-Class Similarity
    Jia-Chen Yang, Yue-Shuang Yang, Yang Li, Shuai Xiao, S. Ercişli · 2022 · Sustainability
45. TL-ADA: Transferable Loss-based Active Domain Adaptation
    Kyeongtak Han, Youngeun Kim, Dongyoon Han, Hojun Lee, Sungeun Hong · 2023 · Neural Networks
46. Active Learning for Stacking and AdaBoost-Related Models
    Qun Sui, Sujit Kumar Ghosh · 2024 · Stats
47. Deep Hashing with Active Pairwise Supervision
    Ziwei Wang, Quan Shui Zheng, Jiwen Lu, Jie Zhou · 2020 · Lecture notes in computer science
48. Bayesian active learning for production, a systematic study and a reusable library
    Parmida Atighehchian, Frederic Branchaud-Charron, Alexandre Lacoste · 2020 · arXiv.org
49. On uncertainty estimation in active learning for image segmentation
    Bo Li, T. Alstrøm · 2020 · arXiv.org
50. BAOD : Budget-Aware Object Detection
    Pardo González, Luis Alejandro · 2024 · instname:Universidad de los Andes
51. FastAL: Fast Evaluation Module for Efficient Dynamic Deep Active Learning Using Broad Learning System
    Shuzhou Sun, Huali Xu, Yan Li, Ping Li, Bin Sheng, Xiao Lin · 2023 · IEEE Transactions on Circuits and Systems for Video Technology
52. Analysis of data‐driven models for predicting fatigue strength of steel components with uncertainty quantification
    Christian Frie, Anton Kolyshkin, Christoph Eberl · 2023 · Fatigue & Fracture of Engineering Materials & Structures
53. FedAL: An Federated Active Learning Framework for Efficient Labeling in Skin Lesion Analysis
    Zhipeng Deng, Yuqiao Yang, Kenji Suzuki, Ze Jin · 2022 · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics
54. Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models
    Guoxuan Xia, Christos-Savvas Bouganis · 2023 · IEEE/CVF International Conference on Computer Vision (ICCV)
55. BALQUE: Batch active learning by querying unstable examples with calibrated confidence
    Yincheng Han, Dajiang Liu, Jiaxing Shang, Linjiang Zheng, Jiang Zhong, Weiwei Cao, Hong Sun, Wu Xie · 2024 · Pattern Recognition
56. Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment
    Mengying Fu, Tianning Yuan, Fang Wan, Songcen Xu, Qixiang Ye · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
57. DEAL: Deep Evidential Active Learning for Image Classification
    Patrick Hemmer, Niklas Kühl, Jakob Schoeffer · 2020 · International Conference on Machine Learning and Applications
58. Active Learning for Deep Visual Tracking
    Di Yuan, Xiaojun Chang, Yi Yang, Qiao Liu, Dehua Wang, Zhenyu He · 2022
59. Enhancing deforestation monitoring in the Brazilian Amazon: A semi-automatic approach leveraging uncertainty estimation
    Jorge Andres Chamorro Martinez, Gilson Alexandre Ostwald Pedro da Costa, Cassiano Gustavo Messias, Luciana de Souza Soler, Claudio Aparecido Almeida, Raul Queiroz Feitosa · 2024 · ISPRS Journal of Photogrammetry and Remote Sensing
60. Idea and Application to Explain Active Learning for Low-Carbon Sustainable AIoT
    Desheng Chen, Shuai Xiao, Meng Xi, Guipeng Lan, Zhuo Zhang · 2024 · IEEE Internet of Things Journal
61. Mitigating Annotation Burden in Active Learning with Transfer Learning and Iterative Acquisition Functions
    Rajiv Avacharmal, Saigurudatta Pamulaparthyvenkata, Piyush Ranjan, Sarika Mulukuntla, A. V. Balakrishnan, P. Preethi, R D Gomathi · 2024 · International Conference on Computing, Communication and Networking Technologies (ICCCNT)
62. Uncertainty pattern and an integration strategy in flood susceptibility modeling: Limited sample size
    Jun Liu, Xueqiang Zhao, Yangbo Chen, Huaizhang Sun, Yu Gu, Shichao Xu · 2025 · Journal of Hydrology
63. HyperspectralViTs: General Hyperspectral Models for On-Board Remote Sensing
    Vít Růžička, Andrew Markham · 2025 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
64. Density-based one-shot active learning for image segmentation
    Qiuye Jin, Shiman Li, Xiaofei Du, Mingzhi Yuan, Manning Wang, Zhijian Song · 2023 · Engineering Applications of Artificial Intelligence
65. Bridging the gap: Active learning for efficient domain adaptation in object detection
    Maximilian Menke, Thomas Wenzel, Andreas Schwung · 2024 · Expert Systems with Applications
66. Uncertainty-Aware Active Domain Adaptive Salient Object Detection
    Guanbin Li, Zhuohua Chen, Mingzhi Mao, Liang Lin, Chaowei Fang · 2024 · IEEE Transactions on Image Processing
67. Unsupervised Fusion Feature Matching for Data Bias in Uncertainty Active Learning
    Wei Huang, Shu-Zhou Sun, Xiao Lin, P. Li, Lei Zhu, Jihong Wang, C. L. Philip Chen, Bin Sheng · 2022 · IEEE Transactions on Neural Networks and Learning Systems
68. Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network
    Hiroto Yamashiro, Atsushi Teramoto, Kuniaki Saito, Hiroshi Fujita · 2021 · Applied Sciences
69. Mitigating Sampling Bias and Improving Robustness in Active Learning
    Ranganath Krishnan, Alok Sinha, Nilesh A. Ahuja, Mahesh Subedar, Omesh Tickoo, R. Iyer · 2021 · arXiv.org
70. Self-supervised class-balanced active learning with uncertainty-mastery fusion
    Yanxue Wu, Fan Min, Gong-Suo Chen, Shao-Peng Shen, Zuo-Cheng Wen, Xiangbing Zhou · 2024 · Knowledge-Based Systems
71. Threshold Active Learning Approach for Physical Violence Detection on Images Obtained from Video (Frame-Level) Using Pre-Trained Deep Learning Neural Network Models
    Itzel M. Abundez, R. Alejo, Francisco Primero Primero, Everardo Efrén Granda-Gutiérrez, Otniel Portillo-Rodríguez, Juan Antonio · 2024 · Algorithms
72. Deep active learning framework for chest-abdominal CT scans segmentation
    Lital Rokach, Yehudit Aperstein, Ayelet Akselrod-Ballin · 2024 · Expert Systems with Applications
73. Two-Stage Active Learning for Efficient Temporal Action Segmentation
    Yan Su, Ehsan Elhamifar · 2024 · Lecture notes in computer science
74. MetaMVUC: Active Learning for Sample-Efficient Sim-to-Real Domain Adaptation in Robotic Grasping
    Maximilian Gilles, Kai Furmans, Rania Rayyes · 2025 · IEEE Robotics and Automation Letters
75. MEAL: Manifold Embedding-based Active Learning
    Deepthi Sreenivasaiah, Johannes Otterbach, Thomas Wollmann · 2021 · IEEE International Conference on Computer Vision Workshops
76. Variational deep embedding-based active learning for the diagnosis of pneumonia
    Jian Long Huang, Wen Ding, Jiarun Zhang, Zhao Li, Ting Shu, Pekka Kuosmanen, Guan‐Qun Zhou, Chuan De Zhou, Gang Yu · 2022 · Frontiers in Neurorobotics
77. Pixel Is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
    Zhenyu Wu, Lin Wang, Wei Wang, Qing Xia, Chenglizhao Chen, Aimin Hao, Shuo Li · 2023 · Proceedings of the AAAI Conference on Artificial Intelligence
78. AL-SAR: Active Learning for Skeleton-Based Action Recognition
    Jingyuan Li, Trung Le, Eli Shlizerman · 2023 · IEEE Transactions on Neural Networks and Learning Systems
79. HandsOff: Labeled Dataset Generation With No Additional Human Annotations
    Austin Xu, Mariya I. Vasileva, Achal Dave, Arjun Seshadri · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
80. BAOD: Budget-Aware Object Detection
    Alejandro Pardo, Mengmeng Xu, Ali Thabet, Pablo Arbeláez, Bernard Ghanem · 2021 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
81. Active semi-supervised expectation maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data
    Phuong Nguyen, David Chapman, Sumeet Menon, Michael A. Morris, Yelena Yesha · 2020 · SPIE Medical Imaging: Computer-Aided Diagnosis
82. Active Semi-Supervised Learning via Bayesian Experimental Design for Lung Cancer Classification Using Low Dose Computed Tomography Scans
    Phuong Nguyen, Ankita Rathod, David Chapman, Smriti Prathapan, Sumeet Menon, Michael A. Morris, Yelena Yesha · 2023 · Applied Sciences
83. AffectFAL: Federated Active Affective Computing with Non-IID Data
    Zixin Zhang, Fan Qi, Shuai Li, Changsheng Xu · 2023 · ACM International Conference on Multimedia (ACM MM)
84. Multi-Task Consistency for Active Learning
    A. Hekimoglu, Philipp Friedrich, W. Zimmer, Michael Schmidt, Alvaro Marcos-Ramiro, Alois C. Knoll · 2023 · 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
85. Diffusion-based Deep Active Learning
    Dan Kushnir, Luca Venturi · 2020 · arXiv (Cornell University)
86. Pre-trained Language Models
    Gerhard Paaß, Sven Giesselbach · 2023 · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms
87. MDAL: Modality-difference-based active learning for multimodal medical image analysis via contrastive learning and pointwise mutual information
    Haoran Wang, Qiuye Jin, Xiaofei Du, Liu Wang, Qinhao Guo, Haiming Li, Manning Wang, Zhijian Song · 2025 · Computerized Medical Imaging and Graphics
88. Active generation network of human skeleton for action recognition
    Xin Wang, Long Liu, Fangming Li, Kai Lu · 2025 · Expert Systems with Applications
89. Towards Balanced Active Learning for Multimodal Classification
    Meng Shen, Yizheng Huang, Jianxiong Yin, Heqing Zou, Deepu Rajan, Simon Chong Wee See · 2023 · ACM International Conference on Multimedia (ACM MM)
90. Active Learning Strategy Using Contrastive Learning and K-means for Aquatic Invasive Species Recognition
    Shaif Chowdhury, Greg Hamerly, Monica E. McGarrity · 2024 · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
91. Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity
    Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, Seiichi Uchida · 2024 · Medical Image Analysis
92. Boosting Semi-supervised Crowd Counting with Scale-based Active Learning
    Shiwei Zhang, Wei Ke, Shuai Liu, Xiaopeng Hong, Tong Zhang · 2024 · ACM International Conference on Multimedia (ACM MM)
93. SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios
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94. VLMine: Long-Tail Data Mining with Vision Language Models
    Mao Ye, Gregory P. Meyer, Zaiwei Zhang, Dennis Park, Siva Karthik Mustikovela, Yuning Chai, Eric M. Wolff · 2025 · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
95. Exploiting Active Learning in Novel Refractive Error Detection with Smartphones
    Eugene Yujun Fu, Zhongqi Yang, Hong Va Leong, Grace Ngai, Chi Wai, Lily Y. L. Chan · 2020 · ACM International Conference on Multimedia (ACM MM)
96. PAL : Pretext-based Active Learning
    Shubhang Bhatnagar, Sachin Goyal, Darshan Mansukhbhai Tank, Amit Sethi · 2021 · British Machine Vision Conference (BMVC)
97. Deep Co-Training Active Learning for Mammographic Images Classification
    Zhikai Yang, Wei Wu, Jingyang Zhang, Yu Zhao, Lixu Gu · 2020 · Chinese Automation Congress
98. Confidence-Calibrated Human Activity Recognition
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99. Sequential Graph Convolutional Network for Active Learning
    Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim · 2020 · Computer Vision and Pattern Recognition
100. Increasing Trustworthiness of Deep Neural Networks via Accuracy Monitoring
     Shao, Zhihui, Jianyi Yang, Shaolei Ren · 2020 · arXiv (Cornell University)