mlsanityData to 14 Oct 2026

Deep Co-Training for Semi-Supervised Image Recognition

arXiv 1803.05984 ↗First posted 15 Mar 2018cs.CVcited by 147

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Abstract (arXiv)

In this paper, we study the problem of semi-supervised image recognition, which is to learn classifiers using both labeled and unlabeled images. We present Deep Co-Training, a deep learning based method inspired by the Co-Training framework. The original Co-Training learns two classifiers on two views which are data from different sources that describe the same instances. To extend this concept to deep learning, Deep Co-Training trains multiple deep neural networks to be the different views and exploits adversarial examples to encourage view difference, in order to prevent the networks from collapsing into each other. As a result, the co-trained networks provide different and complementary information about the data, which is necessary for the Co-Training framework to achieve good results. We test our method on SVHN, CIFAR-10/100 and ImageNet datasets, and our method outperforms the previous state-of-the-art methods by a large margin.

Key facts. Cited by 147 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Mar 2018.

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Citing papers per year: 2021: 32; 2022: 34; 2023: 37; 2024: 23; 2025: 12; 2026: 9.

147 papers.

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  1. 2021Unbiased Teacher for Semi-Supervised Object Detectioncited by 306
  2. 2021A Survey on Deep Semi-supervised Learningcited by 251
  3. 2022Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentationcited by 178
  4. 2024Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Searchcited by 175
  5. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  6. 2021Instant-Teaching: An End-to-End Semi-Supervised Object Detection Frameworkcited by 113
  7. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  8. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 100
  9. 2021One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentationcited by 89
  10. 2021SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentationcited by 86
  11. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 76
  12. 2024UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentationcited by 54
38 more
  1. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  2. 2021A Theory of Label Propagation for Subpopulation Shiftcited by 48
  3. 2022UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentationcited by 45
  4. 2021SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Drivingcited by 43
  5. 2023Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspectivecited by 43
  6. 2021PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learningcited by 41
  7. 2021Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learningcited by 40
  8. 2021Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directionscited by 35
  9. 2023Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentationcited by 35
  10. 2021NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Frameworkcited by 34
  11. 2022SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiescited by 31
  12. 2022Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labelscited by 30
  13. 2022Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentationcited by 28
  14. 2022Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillationcited by 27
  15. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  16. 2023Diverse Cotraining Makes Strong Semi-Supervised Segmentorcited by 26
  17. 2022NP-Match: When Neural Processes meet Semi-Supervised Learningcited by 25
  18. 2024Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoderscited by 25
  19. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  20. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 22
  21. 2021Dual-Consistency Semi-Supervised Learning with Uncertainty Quantification for COVID-19 Lesion Segmentation from CT Imagescited by 22
  22. 2022ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-trainingcited by 20
  23. 2023Semi-supervised learning made simple with self-supervised clusteringcited by 20
  24. 2024Semi-Mamba-UNet: Pixel-Level Contrastive and Pixel-Level Cross-Supervised Visual Mamba-based UNet for Semi-Supervised Medical Image Segmentationcited by 20
  25. 2022Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictorscited by 19
  26. 2021MCDAL: Maximum Classifier Discrepancy for Active Learningcited by 18
  27. 2022A soft nearest-neighbor framework for continual semi-supervised learningcited by 17
  28. 2024DuPL: Dual Student with Trustworthy Progressive Learning for Robust Weakly Supervised Semantic Segmentationcited by 17
  29. 2022Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Trainingcited by 16
  30. 2024A Review of Pseudo-Labeling for Computer Visioncited by 16
  31. 2021Semi-Supervised Learning with Multi-Head Co-Trainingcited by 15
  32. 2022Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentationcited by 15
  33. 2023Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentationcited by 15
  34. 2021DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Imagescited by 14
  35. 2021Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentationcited by 14
  36. 2021GenCo: Generative Co-training for Generative Adversarial Networks with Limited Datacited by 14
  37. 2022Domain Adaptive Hand Keypoint and Pixel Localization in the Wildcited by 14
  38. 2022Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priorscited by 14

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