mlsanityData to 7 Oct 2026

Semi-Supervised Learning with Deep Generative Models

arXiv 1406.5298 ↗First posted 20 Jun 2014cs.LG stat.MLcited by 494

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Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and develop new models that allow for effective generalisation from small labelled data sets to large unlabelled ones. Generative approaches have thus far been either inflexible, inefficient or non-scalable. We show that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.

Key facts. Cited by 494 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2014.

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Citing papers per year: 2019: 1; 2020: 5; 2021: 135; 2022: 119; 2023: 77; 2024: 74; 2025: 55; 2026: 28.

494 papers.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  3. 2021Score-based Generative Modeling in Latent Spacecited by 634
  4. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 445
  5. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  6. 2021A Survey on Deep Semi-supervised Learningcited by 250
  7. 2025LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristicscited by 227
  8. 2021Open-World Semi-Supervised Learningcited by 165
  9. 2021Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalizationcited by 141
  10. 2024Benchmark Data Contamination of Large Language Models: A Surveycited by 138
  11. 2023Equivariant flow matchingcited by 131
  12. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
38 more
  1. 2021ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesiscited by 122
  2. 2021Image-to-Image Translation: Methods and Applicationscited by 116
  3. 2021Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learningcited by 112
  4. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  5. 2022DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latentscited by 103
  6. 2021Mixed supervision for surface-defect detection: from weakly to fully supervised learningcited by 101
  7. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  8. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  9. 2022Poisson Flow Generative Modelscited by 95
  10. 2021CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIPcited by 88
  11. 2022GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Modelscited by 87
  12. 2022Self-Training: A Surveycited by 85
  13. 2021Active Testing: Sample-Efficient Model Evaluationcited by 82
  14. 2025Vision-Language-Action Models for Robotics: A Review Towards Real-World Applicationscited by 82
  15. 2022Local Latent Space Bayesian Optimization over Structured Inputscited by 78
  16. 2022Deep Clustering: A Comprehensive Surveycited by 78
  17. 2021RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learningcited by 72
  18. 2023To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Reviewcited by 72
  19. 2023Diffusion Models as Masked Autoencoderscited by 71
  20. 2022Latent Diffusion Energy-Based Model for Interpretable Text Modelingcited by 67
  21. 2024Towards Generalizable Tumor Synthesiscited by 65
  22. 2021Controllable and Compositional Generation with Latent-Space Energy-Based Modelscited by 64
  23. 2021Hierarchical VAEs Know What They Don't Knowcited by 58
  24. 2021Learning Task Informed Abstractionscited by 58
  25. 2022Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Modelcited by 58
  26. 2023Posterior Collapse and Latent Variable Non-identifiabilitycited by 57
  27. 2023Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Reviewcited by 57
  28. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  29. 2021Consistency Regularization for Variational Auto-Encoderscited by 50
  30. 2021Semi-Supervised Active Learning with Temporal Output Discrepancycited by 50
  31. 2023LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraintscited by 50
  32. 2021Learning Signal-Agnostic Manifolds of Neural Fieldscited by 45
  33. 2021MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networkscited by 44
  34. 2021Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learningcited by 43
  35. 2022ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detectioncited by 43
  36. 2022Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Languagecited by 42
  37. 2024Probabilistic Contrastive Learning for Long-Tailed Visual Recognitioncited by 42
  38. 2021CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learningcited by 41

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