mlsanityData to 14 Oct 2026

Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

arXiv 1706.02690 ↗First posted 8 Jun 2017cs.LG stat.MLcited by 1,328

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Cited by 39 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%.

Key facts. Cited by 1,328 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jun 2017.

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Citing papers per year: 2021: 211; 2022: 241; 2023: 250; 2024: 260; 2025: 211; 2026: 155.

1,328 papers.

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  1. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  2. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  3. 2021A Survey of Uncertainty in Deep Neural Networkscited by 680
  4. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  5. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  6. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  7. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  8. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 353
  9. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  10. 2021What Are Bayesian Neural Network Posteriors Really Like?cited by 308
  11. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  12. 2021Towards Open World Object Detectioncited by 297
38 more
  1. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  2. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 271
  3. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 268
  4. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  5. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 252
  6. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  7. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 216
  8. 2022Extremely Simple Activation Shaping for Out-of-Distribution Detectioncited by 212
  9. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  10. 2022Out-of-Distribution Detection and Selective Generation for Conditional Language Modelscited by 197
  11. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 179
  12. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  13. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  14. 2021Adversarial Reciprocal Points Learning for Open Set Recognitioncited by 153
  15. 2021Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIPcited by 152
  16. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  17. 2022Leveraging Unlabeled Data to Predict Out-of-Distribution Performancecited by 145
  18. 2021OpenGAN: Open-Set Recognition via Open Data Generationcited by 135
  19. 2021Testing for Outliers with Conformal p-valuescited by 134
  20. 2021PixMix: Dreamlike Pictures Comprehensively Improve Safety Measurescited by 131
  21. 2021SPECTRE: Defending Against Backdoor Attacks Using Robust Statisticscited by 130
  22. 2023CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say Nocited by 130
  23. 2023Non-Parametric Outlier Synthesiscited by 129
  24. 2022How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?cited by 128
  25. 2023Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthinesscited by 122
  26. 2021Learning Placeholders for Open-Set Recognitioncited by 121
  27. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  28. 2022Is Out-of-Distribution Detection Learnable?cited by 109
  29. 2022POEM: Out-of-Distribution Detection with Posterior Samplingcited by 108
  30. 2021Semantically Coherent Out-of-Distribution Detectioncited by 107
  31. 2023LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learningcited by 107
  32. 2021Repulsive Deep Ensembles are Bayesiancited by 104
  33. 2024ARGS: Alignment as Reward-Guided Searchcited by 103
  34. 2024Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Modelscited by 103
  35. 2021Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Taskscited by 101
  36. 2021Can multi-label classification networks know what they don't know?cited by 100
  37. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  38. 2021MOOD: Multi-level Out-of-distribution Detectioncited by 94

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