mlsanityData to 7 Oct 2026

Can multi-label classification networks know what they don't know?

arXiv 2109.14162 ↗First posted 29 Sep 2021cs.LGcited by 100

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Abstract (arXiv)

Estimating out-of-distribution (OOD) uncertainty is a central challenge for safely deploying machine learning models in the open-world environment. Improved methods for OOD detection in multi-class classification have emerged, while OOD detection methods for multi-label classification remain underexplored and use rudimentary techniques. We propose JointEnergy, a simple and effective method, which estimates the OOD indicator scores by aggregating energy scores from multiple labels. We show that JointEnergy can be mathematically interpreted from a joint likelihood perspective. Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels. We demonstrate the effectiveness of our method on three common multi-label classification benchmarks, including MS-COCO, PASCAL-VOC, and NUS-WIDE. We show that JointEnergy can reduce the FPR95 by up to 10.05% compared to the previous best baseline, establishing state-of-the-art performance.

Key facts. Cited by 100 papers in the map (data through 7 Oct 2026). Builds on 31 of its 54 references that are in the map. First posted on arXiv 29 Sep 2021.

Builds on

31 of 54 references.

References · 31

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19 more
  1. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  2. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  3. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  4. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  5. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  6. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
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  13. 2017Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classificationcited by 85
  14. 2015Structured Prediction Energy Networkscited by 59
  15. 2013Deep Convolutional Ranking for Multilabel Image Annotationcited by 47
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Citing papers per year: 2021: 6; 2022: 23; 2023: 23; 2024: 28; 2025: 11; 2026: 9.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  2. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  3. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  4. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 338
  5. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 251
  6. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 215
  7. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  8. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  9. 2023In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluationcited by 132
  10. 2023Non-Parametric Outlier Synthesiscited by 129
  11. 2022Is Out-of-Distribution Detection Learnable?cited by 109
  12. 2022POEM: Out-of-Distribution Detection with Posterior Samplingcited by 108
38 more
  1. 2024ARGS: Alignment as Reward-Guided Searchcited by 99
  2. 2022Training OOD Detectors in their Natural Habitatscited by 86
  3. 2021Provable Guarantees for Understanding Out-of-distribution Detectioncited by 78
  4. 2024Process Reward Model with Q-Value Rankingscited by 78
  5. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  6. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  7. 2024Negative Label Guided OOD Detection with Pretrained Vision-Language Modelscited by 66
  8. 2022RankFeat: Rank-1 Feature Removal for Out-of-distribution Detectioncited by 52
  9. 2022Boosting Out-of-distribution Detection with Typical Featurescited by 48
  10. 2022Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognitioncited by 44
  11. 2023Learning to Augment Distributions for Out-of-Distribution Detectioncited by 38
  12. 2023GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detectioncited by 36
  13. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  14. 2024Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Surveycited by 36
  15. 2022Detecting and Recovering Sequential DeepFake Manipulationcited by 34
  16. 2023LINe: Out-of-Distribution Detection by Leveraging Important Neuronscited by 34
  17. 2022OpenCon: Open-world Contrastive Learningcited by 33
  18. 2022Watermarking for Out-of-distribution Detectioncited by 28
  19. 2023Open Set Action Recognition via Multi-Label Evidential Learningcited by 26
  20. 2023Improving Selective Visual Question Answering by Learning from Your Peerscited by 24
  21. 2022Out-of-Distribution Detection with Semantic Mismatch under Maskingcited by 22
  22. 2024Out-of-distribution Detection in Medical Image Analysis: A surveycited by 22
  23. 2023GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradientscited by 21
  24. 2023Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detectioncited by 16
  25. 2023DOS: Diverse Outlier Sampling for Out-of-Distribution Detectioncited by 16
  26. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  27. 2025Out-of-Distribution Detection Methods Answer the Wrong Questionscited by 14
  28. 2023AUTO: Adaptive Outlier Optimization for Test-Time OOD Detectioncited by 12
  29. 2023Uncertainty-aware Unsupervised Multi-Object Trackingcited by 12
  30. 2024Unsolvable Problem Detection: Robust Understanding Evaluation for Large Multimodal Modelscited by 12
  31. 2024Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifiercited by 12
  32. 2024Can OOD Object Detectors Learn from Foundation Models?cited by 10
  33. 2024SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximationcited by 9
  34. 2022YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detectioncited by 7
  35. 2022Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networkscited by 7
  36. 2024Taming False Positives in Out-of-Distribution Detection with Human Feedbackcited by 7
  37. 2024TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detectioncited by 7
  38. 2024When and How Does In-Distribution Label Help Out-of-Distribution Detection?cited by 6

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