mlsanityData to 14 Oct 2026

Managing the unknown: a survey on Open Set Recognition and tangential areas

arXiv 2312.08785 ↗First posted 14 Dec 2023cs.LG cs.CVcited by 4

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Abstract (arXiv)

In real-world scenarios classification models are often required to perform robustly when predicting samples belonging to classes that have not appeared during its training stage. Open Set Recognition addresses this issue by devising models capable of detecting unknown classes from samples arriving during the testing phase, while maintaining a good level of performance in the classification of samples belonging to known classes. This review comprehensively overviews the recent literature related to Open Set Recognition, identifying common practices, limitations, and connections of this field with other machine learning research areas, such as continual learning, out-of-distribution detection, novelty detection, and uncertainty estimation. Our work also uncovers open problems and suggests several research directions that may motivate and articulate future efforts towards more safe Artificial Intelligence methods.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 42 of its 89 references that are in the map. First posted on arXiv 14 Dec 2023.

Builds on

42 of 89 references.

References · 42

  1. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  2. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 872
  3. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  4. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  5. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  6. 2015Towards Open Set Deep Networkscited by 538
  7. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 353
  8. 2018Recent Advances in Open Set Recognition: A Surveycited by 310
  9. 2021How to Train Your Energy-Based Modelscited by 274
  10. 2014Towards Open World Recognitioncited by 226
  11. 2017Generative OpenMax for Multi-Class Open Set Classificationcited by 157
  12. 2019C2AE: Class Conditioned Auto-Encoder for Open-set Recognitioncited by 142
30 more
  1. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  2. 2020Learning Open Set Network with Discriminative Reciprocal Pointscited by 115
  3. 2017DOC: Deep Open Classification of Text Documentscited by 109
  4. 2020Hybrid Models for Open Set Recognitioncited by 109
  5. 2015The Extreme Value Machinecited by 78
  6. 2023Energy-based Out-of-Distribution Detection for Graph Neural Networkscited by 68
  7. 2019Out-of-domain Detection for Natural Language Understanding in Dialog Systemscited by 62
  8. 2022Feature-Realistic Neural Fusion for Real-Time, Open Set Scene Understandingcited by 37
  9. 2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
  10. 2020ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detectioncited by 27
  11. 2022PMAL: Open Set Recognition via Robust Prototype Miningcited by 27
  12. 2019Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognitioncited by 26
  13. 2023Open Set Action Recognition via Multi-Label Evidential Learningcited by 26
  14. 2021A Survey on Open Set Recognitioncited by 25
  15. 2021Adversarially Robust One-class Novelty Detectioncited by 24
  16. 2021SphereFace Revived: Unifying Hyperspherical Face Recognitioncited by 23
  17. 2018Unseen Class Discovery in Open-world Classificationcited by 20
  18. 2019Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?cited by 13
  19. 2022Towards Open-Set Text Recognition via Label-to-Prototype Learningcited by 12
  20. 2023Open-Set Likelihood Maximization for Few-Shot Learningcited by 10
  21. 2020Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognitioncited by 8
  22. 2019Moving Towards Open Set Incremental Learning: Readily Discovering New Authorscited by 5
  23. 2020Deep Active Learning via Open Set Recognitioncited by 5
  24. 2021An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attackscited by 5
  25. 2022Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detectioncited by 4
  26. 2021Opening Deep Neural Networks with Generative Modelscited by 2
  27. 2022Multi-Attribute Open Set Recognitioncited by 2
  28. 2023torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Pythoncited by 2
  29. 2023Master's Thesis: Out-of-distribution Detection with Energy-based Modelscited by 1
  30. 2023On the link between generative semi-supervised learning and generative open-set recognitioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024A Review of Pseudo-Labeling for Computer Visioncited by 16
  2. 2025Sistema de Reconocimiento Facial Federado en Conjuntos Abiertos basado en OpenMaxcited by 0
  3. 2026SeNeDiF-OOD: Semantic Nested Dichotomy Fusion for Out-of-Distribution Detection Methodology in Open-World Classification. A Case Study on Monument Style Classificationcited by 0
  4. 2026Open-Set Vein Biometric Recognition with Deep Metric Learningcited by 0