mlsanityData to 14 Oct 2026

OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection

arXiv 2306.09301 ↗First posted 15 Jun 2023cs.LG cs.CVcited by 179

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Abstract (arXiv)

Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems. Despite the emergence of an increasing number of OOD detection methods, the evaluation inconsistencies present challenges for tracking the progress in this field. OpenOOD v1 initiated the unification of the OOD detection evaluation but faced limitations in scalability and scope. In response, this paper presents OpenOOD v1.5, a significant improvement from its predecessor that ensures accurate and standardized evaluation of OOD detection methodologies at large scale. Notably, OpenOOD v1.5 extends its evaluation capabilities to large-scale data sets (ImageNet) and foundation models (e.g., CLIP and DINOv2), and expands its scope to investigate full-spectrum OOD detection which considers semantic and covariate distribution shifts at the same time. This work also contributes in-depth analysis and insights derived from comprehensive experimental results, thereby enriching the knowledge pool of OOD detection methodologies. With these enhancements, OpenOOD v1.5 aims to drive advancements and offer a more robust and comprehensive evaluation benchmark for OOD detection research.

Key facts. Cited by 179 papers in the map (data through 14 Oct 2026). Builds on 65 of its 96 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

65 of 96 references.

References · 65

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38 more
  1. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,515
  2. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  3. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,432
  4. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
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  14. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
  15. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
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  31. 2022Extremely Simple Activation Shaping for Out-of-Distribution Detectioncited by 212
  32. 2021A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detectioncited by 207
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  34. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
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50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 21; 2024: 52; 2025: 59; 2026: 47.

179 papers.

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  1. 2024ARGS: Alignment as Reward-Guided Searchcited by 103
  2. 2024Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Modelscited by 103
  3. 2024Process Reward Model with Q-Value Rankingscited by 80
  4. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  5. 2023Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancementcited by 77
  6. 2024Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?cited by 61
  7. 2023Fast Decision Boundary based Out-of-Distribution Detectorcited by 49
  8. 2024Learning with Mixture of Prototypes for Out-of-Distribution Detectioncited by 43
  9. 2023NECO: NEural Collapse Based Out-of-distribution detectioncited by 42
  10. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  11. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  12. 2023Detecting Out-of-Distribution Through the Lens of Neural Collapsecited by 34
38 more
  1. 2024Outlier detection by ensembling uncertainty with negative objectnesscited by 34
  2. 2024Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detectioncited by 32
  3. 2024COME: Test-time adaption by Conservatively Minimizing Entropycited by 31
  4. 2023ImageNet-OOD: Deciphering Modern Out-of-Distribution Detection Algorithmscited by 22
  5. 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learningcited by 22
  6. 2024Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domainscited by 22
  7. 2024LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Modelscited by 21
  8. 2025Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learningcited by 21
  9. 2024Unified Entropy Optimization for Open-Set Test-Time Adaptationcited by 19
  10. 2024Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmarkcited by 18
  11. 2025OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionarycited by 17
  12. 2024AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Modelscited by 16
  13. 2024Learning Structured Representations with Hyperbolic Embeddingscited by 16
  14. 2025OpenMIBOOD: Open Medical Imaging Benchmarks for Out-Of-Distribution Detectioncited by 14
  15. 2025Out-of-Distribution Detection Methods Answer the Wrong Questionscited by 14
  16. 2023Adversarial Attacks on Foundational Vision Modelscited by 13
  17. 2024A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?cited by 13
  18. 2024Gradient-Regularized Out-of-Distribution Detectioncited by 13
  19. 2024Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix Itcited by 11
  20. 2025Probing the Representational Power of Sparse Autoencoders in Vision Modelscited by 10
  21. 2024Forte : Finding Outliers with Representation Typicality Estimationcited by 9
  22. 2025CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveragingcited by 9
  23. 2023Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Datacited by 8
  24. 2024On the Learnability of Out-of-distribution Detectioncited by 8
  25. 2024OoDIS: Anomaly Instance Segmentation and Detection Benchmarkcited by 8
  26. 2024Are We Ready for Out-of-Distribution Detection in Digital Pathology?cited by 8
  27. 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamicscited by 8
  28. 2024A Unified Approach Towards Active Learning and Out-of-Distribution Detectioncited by 7
  29. 2024OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learningcited by 7
  30. 2025Joint Out-of-Distribution Filtering and Data Discovery Active Learningcited by 7
  31. 2025AdaSCALE: Adaptive Scaling for OOD Detectioncited by 7
  32. 2024When and How Does In-Distribution Label Help Out-of-Distribution Detection?cited by 6
  33. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  34. 2025Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representationscited by 6
  35. 2025Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradationcited by 6
  36. 2023Classifier-head Informed Feature Masking and Prototype-based Logit Smoothing for Out-of-Distribution Detectioncited by 5
  37. 2023SD-NAE: Generating Natural Adversarial Examples with Stable Diffusioncited by 5
  38. 2023Likelihood-Aware Semantic Alignment for Full-Spectrum Out-of-Distribution Detectioncited by 5

50 most cited of 179 All 179 (JSON)