mlsanityData to 14 Oct 2026

Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data

arXiv 2002.11297 ↗First posted 26 Feb 2020cs.CV cs.LG eess.IVcited by 408

Not on the map. Why?

Abstract (arXiv)

Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade otherwise. Therefore, detecting whether an example is out-of-distribution (OoD) is crucial to enable a system that can reject such samples or alert users. Recent works have made significant progress on OoD benchmarks consisting of small image datasets. However, many recent methods based on neural networks rely on training or tuning with both in-distribution and out-of-distribution data. The latter is generally hard to define a-priori, and its selection can easily bias the learning. We base our work on a popular method ODIN, proposing two strategies for freeing it from the needs of tuning with OoD data, while improving its OoD detection performance. We specifically propose to decompose confidence scoring as well as a modified input pre-processing method. We show that both of these significantly help in detection performance. Our further analysis on a larger scale image dataset shows that the two types of distribution shifts, specifically semantic shift and non-semantic shift, present a significant difference in the difficulty of the problem, providing an analysis of when ODIN-like strategies do or do not work.

Key facts. Cited by 408 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Feb 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 74; 2022: 87; 2023: 85; 2024: 85; 2025: 58; 2026: 19.

408 papers.

Sort:

By area:

  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  2. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  3. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  4. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  5. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  6. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 353
  7. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  8. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  9. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 268
  10. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 252
  11. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  12. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 216
38 more
  1. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  2. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 179
  3. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  4. 2023Towards Open Vocabulary Learning: A Surveycited by 166
  5. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  6. 2021Adversarial Reciprocal Points Learning for Open Set Recognitioncited by 153
  7. 2021Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIPcited by 152
  8. 2023Non-Parametric Outlier Synthesiscited by 129
  9. 2022How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?cited by 128
  10. 2021Machine Learning with a Reject Option: A surveycited by 109
  11. 2022Is Out-of-Distribution Detection Learnable?cited by 109
  12. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 101
  13. 2021Can multi-label classification networks know what they don't know?cited by 100
  14. 2021MOOD: Multi-level Out-of-distribution Detectioncited by 94
  15. 2022Training OOD Detectors in their Natural Habitatscited by 86
  16. 2021Types of Out-of-Distribution Texts and How to Detect Themcited by 80
  17. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  18. 2021Contrastive Out-of-Distribution Detection for Pretrained Transformerscited by 71
  19. 2021Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domaincited by 71
  20. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  21. 2021On the Impact of Spurious Correlation for Out-of-distribution Detectioncited by 68
  22. 2023Energy-based Out-of-Distribution Detection for Graph Neural Networkscited by 68
  23. 2021Towards Discovery and Attribution of Open-world GAN Generated Imagescited by 66
  24. 2021Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environmentscited by 66
  25. 2024Negative Label Guided OOD Detection with Pretrained Vision-Language Modelscited by 66
  26. 2021What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?cited by 63
  27. 2022Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detectioncited by 59
  28. 2021DOCTOR: A Simple Method for Detecting Misclassification Errorscited by 53
  29. 2022RankFeat: Rank-1 Feature Removal for Out-of-distribution Detectioncited by 52
  30. 2023Fast Decision Boundary based Out-of-Distribution Detectorcited by 49
  31. 2021Energy-Based Open-World Uncertainty Modeling for Confidence Calibrationcited by 46
  32. 2022Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systemscited by 44
  33. 2024A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)cited by 44
  34. 2021Neural Mean Discrepancy for Efficient Out-of-Distribution Detectioncited by 42
  35. 2023ID-like Prompt Learning for Few-Shot Out-of-Distribution Detectioncited by 42
  36. 2023Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Modelscited by 41
  37. 2023Anomaly Detection under Distribution Shiftcited by 35
  38. 2023Detecting Out-of-Distribution Through the Lens of Neural Collapsecited by 34

50 most cited of 408 All 408 (JSON)