A Survey of Deep Active Learning

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Items 101–200 of 1,006

1. Sustainable and intelligent time-series models for epidemic disease forecasting and analysis
   Anureet Chhabra, Sarjana Singh, Akash Sharma, Sudhakar Kumar, Brij Bhooshan Gupta, Varsha Arya, Kwok Tai Chui · 2024 · Sustainable Technology and Entrepreneurship
2. A Comprehensive Review of RGBT Tracking
   Haiping Zhang, Di Yuan, Xiu Shu, Zhihui Li, Qiao Liu, Xiaojun Chang, Zhenyu He, Guangming Shi · 2024 · IEEE Transactions on Instrumentation and Measurement
3. A Comprehensive Survey on Evidential Deep Learning and its Applications
   Junyu Gao, Mengyuan Chen, Liangyu Xiang, Changsheng Xu · 2025 · IEEE Transactions on Pattern Analysis and Machine Intelligence
4. Cold-start active learning for image classification
   Qiuye Jin, Mingzhi Yuan, Shiman Li, Haoran Wang, Manning Wang, Zhijian Song · 2022 · Information Sciences
5. Analysis of the Role of Global Information Management in Advanced Decision Support Systems (DSS) for Sustainable Development
   Brij Bhooshan Gupta, Prabin Kumar Panigrahi · 2023 · Journal of Global Information Management
6. Keeping pace with the explosive growth of chemical libraries with structure‐based virtual screening
   Jacqueline Kuan, Mariia Radaeva, Adeline Avenido, Artem R Cherkasov, Francesco Gentile · 2023 · Wiley Interdisciplinary Reviews Computational Molecular Science
7. Metamorphic Malware and Obfuscation: A Survey of Techniques, Variants, and Generation Kits
   Kenneth Brezinski, Ken Ferens · 2023 · Security and Communication Networks
8. A comprehensive review on Federated Learning for Data-Sensitive Application: Open issues & challenges
   Manu Narula, Jasraj Meena, Dinesh Kumar Vishwakarma · 2024 · Engineering Applications of Artificial Intelligence
9. Navigating Challenges and Harnessing Opportunities: Deep Learning Applications in Internet of Medical Things
   John Mulo, Hengshuo Liang, Mian Qian, Milon Biswas, Bharat S. Rawal, Yifan Guo, Wei Yu · 2025 · Future Internet
10. Which images to label for few-shot medical landmark detection?
    Quan Quan, Qingsong Yao, Jun Li, S. Kevin Zhou · 2022 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
11. AI Autonomy: Self‐initiated Open‐world Continual Learning and Adaptation
    Bing Liu, Sahisnu Mazumder, Eric Robertson, Scott Grigsby · 2023 · AI Magazine
12. Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-based Active Learning
    Wei Ji, Renjie Liang, Zhedong Zheng, Wenqiao Zhang, Shengyu Zhang, Juncheng Li, Mengze Li, Tat‐Seng Chua · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
13. Interactive Evolutionary Multiobjective Optimization via Learning to Rank
    Ke Li, Guiyu Lai, Xin Yao · 2023 · IEEE Transactions on Evolutionary Computation
14. Learning from single-defect wafer maps to classify mixed-defect wafer maps
    Jaewoong Shim, Seokho Kang · 2023 · Expert Systems with Applications
15. A Novel Machine-Learning-Based Batch Selection Method in Sparse Near-Field Scanning
    Ling Zhang, Yu-Ru Feng, Bo Pu, Kevin Cai, Da Li, Xing‐Chang Wei, Bhyrav Mutnury, Jun Fan, Hongsheng Chen, James L. Drewniak et al. · 2022 · IEEE Transactions on Microwave Theory and Techniques
16. Modeling, Replicating, and Predicting Human Behavior: A Survey
    Andrew Fuchs, Andrea Passarella, Marco Conti · 2023 · ACM Transactions on Autonomous and Adaptive Systems
17. A deep learning-based approach for crack damage detection using strain field
    Zekai Huang, Dongdong Chang, Xiaofa Yang, Hong Zuo · 2023 · Engineering Fracture Mechanics
18. AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving
    Mingfu Liang, Jong-Chyi Su, Samuel Schulter, Sparsh Garg, Shiyu Zhao, Ying Wu, Manmohan Krishna Chandraker · 2024 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
19. Deep learning in computational dermatopathology of melanoma: A technical systematic literature review
    Daniel Sauter, Georg Christian Lodde, Felix Nensa, Dirk Schadendorf, Elisabeth Livingstone, Markus Kukuk · 2023 · Computers in Biology and Medicine
20. Med-Former: A Transformer Based Architecture for Medical Image Classification
    G. Jignesh Chowdary, Zhaozheng Yin · 2024 · Lecture notes in computer science
21. Multiview Spatial–Spectral Active Learning for Hyperspectral Image Classification
    Meng Xu, Qingqing Zhao, Sen Jia · 2021 · IEEE Transactions on Geoscience and Remote Sensing
22. Bio-inspired Active Learning method in spiking neural network
    Qiugang Zhan, Guisong Liu, Xiurui Xie, Malu Zhang, Guolin Sun · 2022 · Knowledge-Based Systems
23. Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
    Yichen Xie, Han Lü, Junchi Yan, Xiaokang Yang, Masayoshi Tomizuka, Wei Zhan · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
24. A Review of Computing with Spiking Neural Networks
    Jiadong Wu, Yinan Wang, Zhiwei Li, Lun Lu, Qingjiang Li · 2024 · Computers, materials & continua/Computers, materials & continua (Print)
25. Enabling Visual Recognition at Radio Frequency
    Haowen Lai, Gaoxiang Luo, Yifei Liu, Mingmin Zhao · 2024 · ACM Annual International Conference on Mobile Computing and Networking (MobiCom)
26. Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework
    Jianghong Wang, Qiang Xue, Wenjun Zhang, Kelvin K. L. Wong, Zhihua Liu · 2024 · Frontiers in Cardiovascular Medicine
27. Uneven internal SOC distribution estimation of lithium-ion batteries using ultrasonic transmission signals: A new data screening technique and an improved deep residual network
    Ting Hao Tang, Quan Xia, Mingkang Xu, Zhe Deng, Fusheng Jiang, Zeyu Wu, Yi Ren, Dezhen Yang, Cheng Qian · 2025 · eTransportation
28. One-class learning for fake news detection through multimodal variational autoencoders
    Marcos Paulo Silva Gôlo, Mariana Caravanti de Souza, Rafael Geraldeli Rossi, Solange Oliveira Rezende, Bruno Magalhães Nogueira, Ricardo Marcondes Marcacini · 2023 · Engineering Applications of Artificial Intelligence
29. A data-driven model for predicting the mixed-mode stress intensity factors of a crack in composites
    Xinyu Zhang, Tingting Zhao, Yifan Liu, Qingqing Chen, Zhiyong Wang, Zhihua Wang · 2023 · Engineering Fracture Mechanics
30. Federated Active Learning (F-AL): An Efficient Annotation Strategy for Federated Learning
    Jin-Hyun Ahn, Yeeun Ma, Seoyun Park, Cheolwoo You · 2024 · IEEE Access
31. Predicting T cell receptor functionality against mutant epitopes
    Felix Drost, Emilio Dorigatti, Adrian Straub, Philipp Hilgendorf, Karolin I. Wagner, Kersten Heyer, Marta López Montes, Bernd Bischl, Dirk Hans Busch, Kilian Schober et al. · 2024 · Cell Genomics
32. CS-GAN: Cross-Structure Generative Adversarial Networks for Chinese calligraphy translation
    Yun Xiao, Wenlong Lei, Lei Lu, Xiaojun Chang, Xia Zheng, Xiaojiang Chen · 2021 · Knowledge-Based Systems
33. Deep learning to catalyze inverse molecular design
    Abdulelah S. Alshehri, Fengqi You · 2022 · Chemical Engineering Journal
34. Predicting the impact of no. of authors on no. of citations of research publications based on neural networks
    Fatma M. Talaat, Samah Adel Gamel · 2022 · Journal of Ambient Intelligence and Humanized Computing
35. Finding Support Examples for In-Context Learning
    Xiaonan Li, Xi-Peng Qiu · 2023 · Conference on Empirical Methods in Natural Language Processing
36. Towards Active Learning for Action Spotting in Association Football Videos
    Silvio Giancola, Anthony Cioppa, Julia V. Georgieva, Johsan Billingham, Andreas Serner, Kerry Peek, Bernard Ghanem, Marc Van Droogenbroeck · 2023 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
37. Deep indexed active learning for matching heterogeneous entity representations
    Arjit Jain, Sunita Sarawagi, Prithviraj Sen · 2021 · Proceedings of the VLDB Endowment
38. Evaluating Surprise Adequacy for Deep Learning System Testing
    Jin-Han Kim, ROBERT H. FELDT, Shin Yoo · 2022 · ACM Transactions on Software Engineering and Methodology
39. MuL-GRN: Multi-Level Graph Relation Network for Few-Shot Node Classification
    Lingling Zhang, Shaowei Wang, Jun Liu, Qika Lin, Xiaojun Chang, Yaqiang Wu, Qinghua Zheng · 2022 · IEEE Transactions on Knowledge and Data Engineering
40. Box-Level Active Detection
    Mengyao Lyu, Jundong Zhou, Hui Chen, Yijie Huang, Dongdong Yu, Yaqian Li, Yandong Guo, Yuchen Guo, Liuyu Xiang, Guiguang Ding · 2023 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
41. A Discrimination-Guided Active Learning Method Based on Marginal Representations for Industrial Compound Fault Diagnosis
    Zeyi Liu, Jingfei Zhang, Xiao Yang He · 2023 · IEEE Transactions on Automation Science and Engineering
42. Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
    Igor Poltavsky, Mirela Puleva, Anton Charkin-Gorbulin, Grégory Fonseca, Ilyes Batatia, Nicholas J. Browning, Stefan Chmiela, Mengnan Cui, J. Thorben Frank, Stefan Heinen et al. · 2025 · Chemical Science
43. Refining one-class representation: A unified transformer for unsupervised time-series anomaly detection
    Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen · 2023 · Information Sciences
44. Evolution and emerging trends of named entity recognition: Bibliometric analysis from 2000 to 2023
    Jun Yang, Taihua Zhang, Chieh-Yuan Tsai, Yao Lu, Liguo Yao · 2024 · Heliyon
45. Exploring Diversity-Based Active Learning for 3D Object Detection in Autonomous Driving
    Jin-Peng Lin, Zhihao Liang, Sheng Deng, Li-Le Cai, Tao Jiang, Tian-Rui Li, K. Jia, Xun Xu · 2022 · IEEE transactions on intelligent transportation systems (Print)
46. Learning by doing: A dual-loop implementation architecture of deep active learning and human-machine collaboration for smart robot vision
    Wupeng Deng, Quan Liu, Feifan Zhao, Duc Truong Pham, Jiwei Hu, Yongjing Wang, Zude Zhou · 2023 · Robotics and Computer-Integrated Manufacturing
47. Survey of Machine Learning for Software-assisted Hardware Design Verification: Past, Present, and Prospect
    Nan Wu, Yingjie Li, Hang Yang, Hanqiu Chen, Steve X. Dai, Cong Hao, Cunxi Yu, Yuan Xie · 2024 · ACM Transactions on Design Automation of Electronic Systems
48. An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques
    Hamid Gholami, Aliakbar Mohammadifar, Yougui Song, Yue Li, Paria Rahmani, Dimitris G. Kaskaoutis, Panos Panagos, Pasquale Borrelli · 2024 · Scientific Reports
49. iLabel: Revealing Objects in Neural Fields
    Shuaifeng Zhi, Edgar Sucar, André Mouton, I. Haughton, Tristan Laidlow, Andrew J. Davison · 2022 · IEEE Robotics and Automation Letters
50. A Crowd-Ai Dynamic Neural Network Hyperparameter Optimization Approach for Image-Driven Social Sensing Applications
    Yang Zhang, Ruohan Zong, Lanyu Shang, Dong Wang · 2023 · Knowledge-Based Systems
51. Automatic generation of synthetic datasets from a city digital twin for use in the instance segmentation of building facades
    Jiaxin Zhang, Tomohiro Fukuda, Nobuyoshi Yabuki · 2022 · Journal of Computational Design and Engineering
52. High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons
    Richard K. F. Nair, Martin Strube, Martin Hertel, Olaf Kolle, Víctor Rolo, Mirco Migliavacca · 2022 · Journal of Experimental Botany
53. Deep Active Learning for Computer Vision: Past and Future
    Takezoe, Rinyoichi, Liu, Xu, Mao, Shunan, Chen, Marco Tianyu, Feng, Zhanpeng, Zhang, Shiliang, Wang, Xiaoyu · 2023 · APSIPA Transactions on Signal and Information Processing
54. Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation
    Luca Rettenberger, Marcel P. Schilling, Stefan Elser, Moritz Böhland, Markus Reischl · 2023 · IEEE Transactions on Biomedical Engineering
55. GraphPrior: Mutation-based Test Input Prioritization for Graph Neural Networks
    Xueqi Dang, Yinghua Li, Mike Papadakis, Jacques Klein, Tegawendé François Bissyandé, Yves Le Traon · 2023 · ACM Transactions on Software Engineering and Methodology
56. SALAD: A split active learning based unsupervised network data stream anomaly detection method using autoencoders
    Christopher Nixon, Mohamed Sedky, Justin Champion, Mohamed Hassan · 2024 · Expert Systems with Applications
57. Active learning framework for tunnel geological reconstruction based on TBM operational data
    Hao Wang, Lixue Liu, Maolin Shi, Jie Yang, Xueguan Song, Chao Zhang, Dacheng Tao · 2023 · Automation in Construction
58. Machine learning robustness: a primer
    Houssem Ben Braiek, Foutse Khomh · 2025 · Elsevier eBooks
59. Surveying the deep: A review of computer vision in the benthos
    Cameron Trotter, Huw J. Griffiths, Rowan J. Whittle · 2025 · Ecological Informatics
60. The paradigm of hallucinations in AI-driven cybersecurity systems: Understanding taxonomy, classification outcomes, and mitigations
    Sood, Aditya K, Zeadally, Sherali, Hong, EenKee · 2025 · Computers and Electrical Engineering
61. Machine learning for multiscale modeling in computational molecular design
    Abdulelah S. Alshehri, Fengqi You · 2021 · Current Opinion in Chemical Engineering
62. Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence
    Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari · 2022 · Environmental Science and Pollution Research
63. DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions for the Visual Exploration of Small Multiples
    Qianwen Wang, Sehi L’Yi, Nils Gehlenborg · 2023 · CHI Conference on Human Factors in Computing Systems
64. Real-Time Object Detection for Autonomous Solar Farm Inspection via UAVs
    Javier Rodríguez-Vázquez, Inés Prieto-Centeno, Miguel Fernández-Cortizas, David Pérez-Saura, Martín Molina, Pascual Campoy · 2024 · Sensors
65. Powering Electricity Forecasting with Transfer Learning
    Firuz Kamalov, Hana Sulieman, Sherif Moussa, Jorge Avante Reyes, Murodbek Safaraliev · 2024 · Energies
66. Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
    Yuecong Xu, Haozhi Cao, Li-Hua Xie, Xiao-Li Li, Z. Chen, Jianfei Yang · 2022 · ACM Computing Surveys
67. Toward domain adaptation with open-set target data: Review of theory and computer vision applications
    Reyhane Ghaffari, Mohammad Sadegh Helfroush, Abbas Khosravi, Kamran Kazemi, Habibollah Danyali, Leszek Rutkowski · 2023 · Information Fusion
68. Toward global rooftop PV detection with Deep Active Learning
    Matthias Zech, Hendrik-Pieter Tetens, Joseph A. Ranalli · 2024 · Advances in Applied Energy
69. LAMBO: Large AI Model Empowered Edge Intelligence
    Dong Li, Feibo Jiang, Yubo Peng, Kezhi Wang, Kun Yang, Cunhua Pan, Robert Schober · 2024 · IEEE Communications Magazine
70. Deep Bayesian active learning using in-memory computing hardware
    Yudeng Lin, Bin Gao, Jianshi Tang, Qingtian Zhang, He Qian, Huaqiang Wu · 2024 · Nature Computational Science
71. ActiveEA: Active Learning for Neural Entity Alignment
    Bing Liu, Harrisen Scells, Guido Zuccon, Wen Hua, Genghong Zhao · 2021 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
72. Plant phenotyping with limited annotation: Doing more with less
    Koushik Nagasubramanian, Sarah E. Jones, Asheesh Kumar Singh, Arti Singh, Soumik Sarkar, Baskar Ganapathysubramanian · 2022 · The Plant Phenome Journal
73. Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery
    Hyunho Lee, Wen-Wen Li · 2024 · Remote Sensing of Environment
74. Active Learning for Lane Detection: A Knowledge Distillation Approach
    Fengchao Peng, Chao Wang, Jianzhuang Liu, Zhen Yang · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
75. A patch distribution-based active learning method for multiple instance Alzheimer's disease diagnosis
    Tianxiang Wang, Qun Dai · 2024 · Pattern Recognition
76. Computer-Aided Breast Cancer Diagnosis: Comparative Analysis of Breast Imaging Modalities and Mammogram Repositories
    Parita Oza, Paawan Sharma, Samir Patel, Pankaj Kumar · 2022 · Current Medical Imaging Formerly Current Medical Imaging Reviews
77. Using an Ontology-Based Neural Network and DEA to Discover Deficiencies of Hotel Services
    Tzu-An Chiang, Z.H. Che, Yi-Ling Huang, Chang-You Tsai · 2022 · International Journal on Semantic Web and Information Systems
78. Active learning for Arabic sentiment analysis
    Abdelrahman Kaseb, Mona Farouk · 2023 · Alexandria Engineering Journal
79. FedAWR: An Interactive Federated Active Learning Framework for Air Writing Recognition
    Xiangjie Kong, Wenyi Zhang, Youyang Qu, Xin‐Wei Yao, Guojiang Shen · 2023 · IEEE Transactions on Mobile Computing
80. Distributed Learning in the IoT–Edge–Cloud Continuum
    Audris Arzovs, Jānis Judvaitis, Krišjānis Nesenbergs, Leo Selavo · 2024 · Machine Learning and Knowledge Extraction
81. Leveraging small-scale datasets for additive manufacturing process modeling and part certification: Current practice and remaining gaps
    Durant Fullington, Emmanuel Yangue, Mahathir Mohammad Bappy, Chenang Liu, Wenmeng Tian · 2024 · Journal of Manufacturing Systems
82. Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
    Theodoros Petropoulos, Lefteris Th. Benos, Remigio Berruto, Gabriele Miserendino, Vasso Marinoudi, Patrizia Busato, Chrysostomos Zisis, Dionysis Bochtis · 2025 · Applied Sciences
83. Enhancement of evaluating flatfoot on a weight-bearing lateral radiograph of the foot with U-Net based semantic segmentation on the long axis of tarsal and metatarsal bones in an active learning manner
    Seung Min Ryu, Keewon Shin, Soo Wung Shin, Seung‐Jun Lee, Namkug Kim · 2022 · Computers in Biology and Medicine
84. An active learning framework featured Monte Carlo dropout strategy for deep learning-based semantic segmentation of concrete cracks from images
    J. Chow, Wong Cho Hin Peter, P. Tan, Tun-Jian Tan, Zhaofeng Li, Yu-Hsing Wang · 2023 · Structural Health Monitoring
85. Scaling whale monitoring using deep learning: A human-in-the-loop solution for analyzing aerial datasets
    Justine Boulent, Bertrand Charry, Malcolm McHugh Kennedy, Emily Charry Tissier, Raina Fan, Marianne Marcoux, Cortney A. Watt, Antoine Gagné-Turcotte · 2023 · Frontiers in Marine Science
86. Active learning-based hyperspectral image classification: a reinforcement learning approach
    Usha R. Patel, Vibha D. Patel · 2023 · The Journal of Supercomputing
87. Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies
    Sayedali Shetab Boushehri, Katharina Essig, Nikolaos‐Kosmas Chlis, Sylvia Herter, Marina Bacac, Fabian Joachim Theis, Elke Glasmacher, Carsten Marr, Fabian Schmich · 2023 · Nature Communications
88. An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming
    Giorgia Nadizar, Luigi Rovito, Andrea De Lorenzo, Eric Medvet, Marco Virgolin · 2024 · ACM Transactions on Evolutionary Learning and Optimization
89. Monocular 3D Lane Detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks
    Fulong Ma, Weiqing Qi, Guoyang Zhao, Linwei Zheng, Sheng Wang, Yuxuan Liu, Ming Liu, Jun Ma · 2025 · IEEE Transactions on Intelligent Transportation Systems
90. Deep Learning-Enabled Technologies for Bioimage Analysis
    Fazle Rabbi, Sajjad Rahmani Dabbagh, Pelin Angın, Ali K. Yetisen, Savaş Taşoğlu · 2022 · Micromachines
91. A comprehensive review: active learning for hyperspectral image classifications
    Usha Patel, Vibha D. Patel · 2023 · Earth Science Informatics
92. Shaping the concentration of petroleum hydrocarbon pollution in soil: A machine learning and resistivity-based prediction method
    Fansong Meng, Jinguo Wang, Zhou Chen, Fei Qiao, Dong Gyu Yang · 2023 · Journal of Environmental Management
93. Active learning concerning sampling cost for enhancing AI-enabled building energy system modeling
    Ao Li, Fu Xiao, Ziwei Xiao, Rui Yan, Anbang Li, Yan Lv, Bing Su · 2024 · Advances in Applied Energy
94. Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review
    Ruyue Xin, Jingye Wang, Peng Chen, Zhiming Zhao · 2025 · ACM Computing Surveys
95. ILabel: Interactive Neural Scene Labelling
    Shuaifeng Zhi, Edgar Sucar, André Mouton, I. Haughton, Tristan Laidlow, Andrew J. Davison · 2021 · arXiv (Cornell University)
96. AcLog: An Approach to Detecting Anomalies from System Logs with Active Learning
    Duan, Chiming, Jia, Tong, Li, Ying, Huang, Gang · 2023 · 2023 IEEE International Conference on Web Services (ICWS)
97. Hybrid Active Learning via Deep Clustering for Video Action Detection
    A. Rana, Y. Rawat · 2023 · Computer Vision and Pattern Recognition
98. A Survey of Deep Active Learning for Foundation Models
    Tianjiao Wan, Kele Xu, Ting Yu, Xu Wang, Dawei Feng, Bo Yang Ding, Huaimin Wang · 2023 · Intelligent Computing
99. TiDAL: Learning Training Dynamics for Active Learning
    Seong Min Kye, Kwanghee Choi, Hyeongmin Byun, Buru Chang · 2023 · IEEE/CVF International Conference on Computer Vision (ICCV)
100. Unlabeled learning algorithms and operations: overview and future trends in defense sector
     Eduardo e Oliveira, Marco Rodrigues, João Paulo Pereira, António Mendes Lopes, Ivana Ilic Mestric, S. Bjelogrlic · 2024 · Artificial Intelligence Review