mlsanityData to 7 Oct 2026

Improving Open-Set Semi-Supervised Learning with Self-Supervision

arXiv 2301.10127 ↗First posted 24 Jan 2023cs.LG cs.CV stat.MLcited by 6

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Abstract (arXiv)

Open-set semi-supervised learning (OSSL) embodies a practical scenario within semi-supervised learning, wherein the unlabeled training set encompasses classes absent from the labeled set. Many existing OSSL methods assume that these out-of-distribution data are harmful and put effort into excluding data belonging to unknown classes from the training objective. In contrast, we propose an OSSL framework that facilitates learning from all unlabeled data through self-supervision. Additionally, we utilize an energy-based score to accurately recognize data belonging to the known classes, making our method well-suited for handling uncurated data in deployment. We show through extensive experimental evaluations that our method yields state-of-the-art results on many of the evaluated benchmark problems in terms of closed-set accuracy and open-set recognition when compared with existing methods for OSSL. Our code is available at https://github.com/walline/ssl-tf2-sefoss.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 35 of its 56 references that are in the map. First posted on arXiv 24 Jan 2023.

Builds on

35 of 56 references.

References · 35

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  12. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
23 more
  1. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  2. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  3. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  4. 2021FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labelingcited by 537
  5. 2015Towards Open Set Deep Networkscited by 533
  6. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  7. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 351
  8. 2015Semi-Supervised Learning with Ladder Networkscited by 246
  9. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  10. 2018Open Set Domain Adaptation by Backpropagationcited by 150
  11. 2021Dash: Semi-Supervised Learning with Dynamic Thresholdingcited by 147
  12. 2022SimMatch: Semi-supervised Learning with Similarity Matchingcited by 118
  13. 2020Learning Open Set Network with Discriminative Reciprocal Pointscited by 115
  14. 2020Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learningcited by 82
  15. 2022Class-Aware Contrastive Semi-Supervised Learningcited by 62
  16. 2021Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learningcited by 40
  17. 2021OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Datacited by 22
  18. 2020They are Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learningcited by 18
  19. 2021DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samplescited by 18
  20. 2022Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Datacited by 11
  21. 2022DoubleMatch: Improving Semi-Supervised Learning with Self-Supervisioncited by 8
  22. 2020How Out-of-Distribution Data Hurts Semi-Supervised Learningcited by 6
  23. 2023On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learningcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 2024A Review of Pseudo-Labeling for Computer Visioncited by 16
  2. 2023Semi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Imagescited by 4
  3. 2024ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detectioncited by 3
  4. 2025Self-Supervised Learning for Image Segmentation: A Comprehensive Surveycited by 3
  5. 2024'No' Matters: Out-of-Distribution Detection in Multimodality Long Dialoguecited by 0
  6. 2025Let the Void Be Void: Robust Open-Set Semi-Supervised Learning via Selective Non-Alignmentcited by 0