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Interpolation Consistency Training for Semi-Supervised Learning

arXiv 1903.03825 ↗First posted 9 Mar 2019stat.ML cs.AI cs.LGcited by 251

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Abstract (arXiv)

We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. Our experiments show that ICT achieves state-of-the-art performance when applied to standard neural network architectures on the CIFAR-10 and SVHN benchmark datasets. Our theoretical analysis shows that ICT corresponds to a certain type of data-adaptive regularization with unlabeled points which reduces overfitting to labeled points under high confidence values.

Key facts. Cited by 251 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Mar 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 63; 2022: 66; 2023: 48; 2024: 40; 2025: 21; 2026: 13.

251 papers.

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  1. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  2. 2021A Fourier-based Framework for Domain Generalizationcited by 290
  3. 2021A Survey on Deep Semi-supervised Learningcited by 251
  4. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  5. 2021Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samplescited by 114
  6. 2024TabDPT: Scaling Tabular Foundation Models on Real Datacited by 113
  7. 2021GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injectioncited by 102
  8. 2021Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformercited by 100
  9. 2021SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentationcited by 86
  10. 2021PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Trainingcited by 80
  11. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68
  12. 2022SVFormer: Semi-supervised Video Transformer for Action Recognitioncited by 68
38 more
  1. 2021ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learningcited by 65
  2. 2022Active Learning by Feature Mixingcited by 63
  3. 2022Learning with Neighbor Consistency for Noisy Labelscited by 55
  4. 2022BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotationcited by 54
  5. 2021Local Augmentation for Graph Neural Networkscited by 53
  6. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  7. 2021DINE: Domain Adaptation from Single and Multiple Black-box Predictorscited by 51
  8. 2021Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Datacited by 50
  9. 2022Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularizationcited by 50
  10. 2022Semi-supervised Vision Transformers at Scalecited by 49
  11. 2023STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tablescited by 49
  12. 2022Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Modulecited by 44
  13. 2021OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outlierscited by 43
  14. 2021Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentationcited by 43
  15. 2023Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspectivecited by 43
  16. 2021ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenationcited by 41
  17. 2021Adaptive Consistency Regularization for Semi-Supervised Transfer Learningcited by 37
  18. 2022OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learningcited by 37
  19. 2021A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classificationcited by 36
  20. 2021AutoDropout: Learning Dropout Patterns to Regularize Deep Networkscited by 35
  21. 2021SelfMatch: Combining Contrastive Self-Supervision and Consistency for Semi-Supervised Learningcited by 35
  22. 2021Revisiting Consistency Regularization for Semi-Supervised Learningcited by 34
  23. 2021All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-trainingcited by 32
  24. 2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 31
  25. 2022Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Imagescited by 30
  26. 2022Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labelscited by 30
  27. 2024Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentationcited by 29
  28. 2022Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillationcited by 27
  29. 2022Towards Realistic Semi-Supervised Learningcited by 27
  30. 2023BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Datasetcited by 27
  31. 2021Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimationcited by 25
  32. 2021All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-supervised Medical Image Segmentationcited by 25
  33. 2022ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularizationcited by 25
  34. 2022Contrastive Regularization for Semi-Supervised Learningcited by 24
  35. 2022Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learningcited by 23
  36. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  37. 2022MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularizationcited by 22
  38. 2022MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recoverycited by 22

50 most cited of 251 All 251 (JSON)