mlsanityData to 14 Oct 2026

Learning Confidence for Out-of-Distribution Detection in Neural Networks

arXiv 1802.04865 ↗First posted 13 Feb 2018stat.ML cs.LGcited by 245

Not on the map. Why?

Abstract (arXiv)

Modern neural networks are very powerful predictive models, but they are often incapable of recognizing when their predictions may be wrong. Closely related to this is the task of out-of-distribution detection, where a network must determine whether or not an input is outside of the set on which it is expected to safely perform. To jointly address these issues, we propose a method of learning confidence estimates for neural networks that is simple to implement and produces intuitively interpretable outputs. We demonstrate that on the task of out-of-distribution detection, our technique surpasses recently proposed techniques which construct confidence based on the network's output distribution, without requiring any additional labels or access to out-of-distribution examples. Additionally, we address the problem of calibrating out-of-distribution detectors, where we demonstrate that misclassified in-distribution examples can be used as a proxy for out-of-distribution examples.

Key facts. Cited by 245 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Feb 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 53; 2022: 55; 2023: 49; 2024: 39; 2025: 30; 2026: 19.

245 papers.

Sort:

By area:

  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  2. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  3. 2022OpenOOD: Benchmarking Generalized Out-of-Distribution Detectioncited by 268
  4. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  5. 2023A Survey of Confidence Estimation and Calibration in Large Language Modelscited by 256
  6. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 216
  7. 2021Deep Deterministic Uncertainty: A Simple Baselinecited by 200
  8. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 179
  9. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  10. 2021PixMix: Dreamlike Pictures Comprehensively Improve Safety Measurescited by 131
  11. 2022How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?cited by 128
  12. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
38 more
  1. 2021MOOD: Multi-level Out-of-distribution Detectioncited by 94
  2. 2023A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validationcited by 93
  3. 2021Soft Calibration Objectives for Neural Networkscited by 92
  4. 2022GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Modelscited by 87
  5. 2023Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancementcited by 77
  6. 2024API Is Enough: Conformal Prediction for Large Language Models Without Logit-Accesscited by 69
  7. 2023Energy-based Out-of-Distribution Detection for Graph Neural Networkscited by 68
  8. 2022RbA: Segmenting Unknown Regions Rejected by Allcited by 67
  9. 2024Negative Label Guided OOD Detection with Pretrained Vision-Language Modelscited by 66
  10. 2022The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generationcited by 60
  11. 2021Hierarchical VAEs Know What They Don't Knowcited by 58
  12. 2021DOCTOR: A Simple Method for Detecting Misclassification Errorscited by 53
  13. 2022A Call to Reflect on Evaluation Practices for Failure Detection in Image Classificationcited by 52
  14. 2022A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibrationcited by 49
  15. 2023Fast Decision Boundary based Out-of-Distribution Detectorcited by 49
  16. 2021DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimationcited by 48
  17. 2021OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outlierscited by 43
  18. 2021Neural Mean Discrepancy for Efficient Out-of-Distribution Detectioncited by 42
  19. 2022DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognitioncited by 42
  20. 2023NECO: NEural Collapse Based Out-of-distribution detectioncited by 42
  21. 2021Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentationcited by 40
  22. 2022Expanding Low-Density Latent Regions for Open-Set Object Detectioncited by 38
  23. 2023Trainable Projected Gradient Method for Robust Fine-tuningcited by 37
  24. 2024Overcoming Common Flaws in the Evaluation of Selective Classification Systemscited by 37
  25. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  26. 2021SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detectioncited by 34
  27. 2023LINe: Out-of-Distribution Detection by Leveraging Important Neuronscited by 34
  28. 2023Detecting Out-of-Distribution Through the Lens of Neural Collapsecited by 34
  29. 2024Outlier detection by ensembling uncertainty with negative objectnesscited by 34
  30. 2021Teaching Humans When To Defer to a Classifier via Exemplarscited by 29
  31. 2022Watermarking for Out-of-distribution Detectioncited by 28
  32. 2023Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalizationcited by 28
  33. 2021Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approachescited by 27
  34. 2022Towards Improving Calibration in Object Detection Under Domain Shiftcited by 26
  35. 2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 25
  36. 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teachercited by 25
  37. 2024Open-World Semantic Segmentation Including Class Similaritycited by 24
  38. 2022Few-shot Learning with Noisy Labelscited by 23

50 most cited of 245 All 245 (JSON)