mlsanityData to 7 Oct 2026

An Overview of Deep Semi-Supervised Learning

arXiv 2006.05278 ↗First posted 9 Jun 2020cs.LG stat.MLcited by 121

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Abstract (arXiv)

Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e.g., image classification) when trained on extensive collections of labeled data (e.g., ImageNet). However, creating such large datasets requires a considerable amount of resources, time, and effort. Such resources may not be available in many practical cases, limiting the adoption and the application of many deep learning methods. In a search for more data-efficient deep learning methods to overcome the need for large annotated datasets, there is a rising research interest in semi-supervised learning and its applications to deep neural networks to reduce the amount of labeled data required, by either developing novel methods or adopting existing semi-supervised learning frameworks for a deep learning setting. In this paper, we provide a comprehensive overview of deep semi-supervised learning, starting with an introduction to the field, followed by a summarization of the dominant semi-supervised approaches in deep learning.

Key facts. Cited by 121 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2020.

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Citing papers per year: 2021: 15; 2022: 26; 2023: 34; 2024: 28; 2025: 13; 2026: 5.

121 papers.

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  1. 2023PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimizationcited by 311
  2. 2021A Survey on Deep Semi-supervised Learningcited by 250
  3. 2022Towards Understanding Grokking: An Effective Theory of Representation Learningcited by 229
  4. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  5. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  6. 2022Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationcited by 93
  7. 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
  8. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 74
  9. 2022Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentationcited by 64
  10. 2022Data Feedback Loops: Model-driven Amplification of Dataset Biasescited by 59
  11. 2021Deep Spoken Keyword Spotting: An Overviewcited by 43
  12. 2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
38 more
  1. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  2. 2021Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directionscited by 35
  3. 2024SelEx: Self-Expertise in Fine-Grained Generalized Category Discoverycited by 34
  4. 2022Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentationcited by 30
  5. 2022Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectoriescited by 27
  6. 2023Diverse Cotraining Makes Strong Semi-Supervised Segmentorcited by 26
  7. 2024Semi-supervised Concept Bottleneck Modelscited by 21
  8. 2024Weak-to-Strong Generalization Through the Data-Centric Lenscited by 21
  9. 2023A Survey of Label-Efficient Deep Learning for 3D Point Cloudscited by 20
  10. 2023LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learningcited by 20
  11. 2023Few Shot Medical Image Segmentation with Cross Attention Transformercited by 19
  12. 2022An Information-theoretical Approach to Semi-supervised Learning under Covariate-shiftcited by 18
  13. 2023Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learningcited by 17
  14. 2022Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Studentscited by 16
  15. 2023A Survey on Semi-Supervised Semantic Segmentationcited by 16
  16. 2024A Review of Pseudo-Labeling for Computer Visioncited by 16
  17. 2022Semi-Supervised Hierarchical Graph Classificationcited by 15
  18. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
  19. 2022Important Object Identification with Semi-Supervised Learning for Autonomous Drivingcited by 13
  20. 2024Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2cited by 12
  21. 2021A Comparative Review of Recent Few-Shot Object Detection Algorithmscited by 10
  22. 2021Uncertainty-Aware Temporal Self-Learning (UATS): Semi-Supervised Learning for Segmentation of Prostate Zones and Beyondcited by 9
  23. 2022A Brief Overview of Unsupervised Neural Speech Representation Learningcited by 9
  24. 2023Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognitioncited by 9
  25. 2024BaCon: Boosting Imbalanced Semi-supervised Learning via Balanced Feature-Level Contrastive Learningcited by 9
  26. 2023Instructor-inspired Machine Learning for Robust Molecular Property Predictioncited by 8
  27. 2024MORPH: Towards Automated Concept Drift Adaptation for Malware Detectioncited by 8
  28. 2024BenthicNet: A global compilation of seafloor images for deep learning applicationscited by 7
  29. 2022S5Mars: Semi-Supervised Learning for Mars Semantic Segmentationcited by 6
  30. 2022FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentationcited by 6
  31. 2023Towards Unbiased Training in Federated Open-world Semi-supervised Learningcited by 6
  32. 2024BEM: Balanced and Entropy-based Mix for Long-Tailed Semi-Supervised Learningcited by 6
  33. 2022How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?cited by 5
  34. 2023Semi-supervised Object Detection: A Survey on Recent Research and Progresscited by 5
  35. 2023Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learningcited by 5
  36. 2023CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classificationcited by 5
  37. 2024LaneCorrect: Self-supervised Lane Detectioncited by 5
  38. 2024Towards Understanding Why FixMatch Generalizes Better Than Supervised Learningcited by 5

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