mlsanityData to 7 Oct 2026

Label Propagation for Deep Semi-supervised Learning

arXiv 1904.04717 ↗First posted 9 Apr 2019cs.CV cs.LGcited by 287

Not on the map. Why?

Abstract (arXiv)

Semi-supervised learning is becoming increasingly important because it can combine data carefully labeled by humans with abundant unlabeled data to train deep neural networks. Classic methods on semi-supervised learning that have focused on transductive learning have not been fully exploited in the inductive framework followed by modern deep learning. The same holds for the manifold assumption---that similar examples should get the same prediction. In this work, we employ a transductive label propagation method that is based on the manifold assumption to make predictions on the entire dataset and use these predictions to generate pseudo-labels for the unlabeled data and train a deep neural network. At the core of the transductive method lies a nearest neighbor graph of the dataset that we create based on the embeddings of the same network.Therefore our learning process iterates between these two steps. We improve performance on several datasets especially in the few labels regime and show that our work is complementary to current state of the art.

Key facts. Cited by 287 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 67; 2022: 69; 2023: 59; 2024: 41; 2025: 31; 2026: 20.

287 papers.

Sort:

By area:

  1. 2021Extract Free Dense Labels from CLIPcited by 580
  2. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
  3. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  4. 2021A Survey on Deep Semi-supervised Learningcited by 250
  5. 2022Parameter-free Online Test-time Adaptationcited by 184
  6. 2021A Unified Objective for Novel Class Discoverycited by 161
  7. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  8. 2021Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Timecited by 140
  9. 2023SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learningcited by 133
  10. 2021Humble Teachers Teach Better Students for Semi-Supervised Object Detectioncited by 115
  11. 2021Instant-Teaching: An End-to-End Semi-Supervised Object Detection Frameworkcited by 112
  12. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
38 more
  1. 2022PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discoverycited by 85
  2. 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
  3. 2021ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learningcited by 68
  4. 2021SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classificationcited by 68
  5. 2022Test-Time Adaptation via Self-Training with Nearest Neighbor Informationcited by 64
  6. 2022Debiased Self-Training for Semi-Supervised Learningcited by 62
  7. 2022Class-Aware Contrastive Semi-Supervised Learningcited by 62
  8. 2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
  9. 2021A Closer Look at Self-training for Zero-Label Semantic Segmentationcited by 57
  10. 2021Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoodscited by 57
  11. 2022Learning with Neighbor Consistency for Noisy Labelscited by 55
  12. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  13. 2021DINE: Domain Adaptation from Single and Multiple Black-box Predictorscited by 51
  14. 2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
  15. 2021Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learningcited by 40
  16. 2021More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrievalcited by 38
  17. 2024A Comprehensive Survey on Data Augmentationcited by 38
  18. 2023Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labelscited by 37
  19. 2021Revisiting Consistency Regularization for Semi-Supervised Learningcited by 34
  20. 2023ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptationcited by 34
  21. 2021Reference-guided Pseudo-Label Generation for Medical Semantic Segmentationcited by 33
  22. 2021All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-trainingcited by 32
  23. 2022Towards Semi-Supervised Deep Facial Expression Recognition with An Adaptive Confidence Margincited by 32
  24. 2023Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuningcited by 30
  25. 2023Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentationcited by 29
  26. 2021Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Trainingcited by 28
  27. 2021The Rich Get Richer: Disparate Impact of Semi-Supervised Learningcited by 28
  28. 2024Label Propagation for Zero-shot Classification with Vision-Language Modelscited by 28
  29. 2021Class-Balanced Distillation for Long-Tailed Visual Recognitioncited by 27
  30. 2023CAT: A Contextualized Conceptualization and Instantiation Framework for Commonsense Reasoningcited by 25
  31. 2022Contrastive Regularization for Semi-Supervised Learningcited by 24
  32. 2022Acknowledging the Unknown for Multi-label Learning with Single Positive Labelscited by 24
  33. 2023Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detectioncited by 24
  34. 2023Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Frameworkcited by 24
  35. 2023Image Data Augmentation Approaches: A Comprehensive Survey and Future directionscited by 23
  36. 2023Data-centric Graph Learning: A Surveycited by 22
  37. 2023SPA: A Graph Spectral Alignment Perspective for Domain Adaptationcited by 22
  38. 2021MUM : Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detectioncited by 21

50 most cited of 287 All 287 (JSON)