mlsanityData to 7 Oct 2026

Heatmap-based Out-of-Distribution Detection

arXiv 2211.08115 ↗First posted 15 Nov 2022cs.CVcited by 9

Not on the map. Why?

Abstract (arXiv)

Our work investigates out-of-distribution (OOD) detection as a neural network output explanation problem. We learn a heatmap representation for detecting OOD images while visualizing in- and out-of-distribution image regions at the same time. Given a trained and fixed classifier, we train a decoder neural network to produce heatmaps with zero response for in-distribution samples and high response heatmaps for OOD samples, based on the classifier features and the class prediction. Our main innovation lies in the heatmap definition for an OOD sample, as the normalized difference from the closest in-distribution sample. The heatmap serves as a margin to distinguish between in- and out-of-distribution samples. Our approach generates the heatmaps not only for OOD detection, but also to indicate in- and out-of-distribution regions of the input image. In our evaluations, our approach mostly outperforms the prior work on fixed classifiers, trained on CIFAR-10, CIFAR-100 and Tiny ImageNet. The code is publicly available at: https://github.com/jhornauer/heatmap_ood.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 28 of its 38 references that are in the map. First posted on arXiv 15 Nov 2022.

Builds on

28 of 38 references.

References · 28

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16 more
  1. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  2. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  3. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  4. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  5. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  6. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  7. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  8. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  9. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
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  11. 2020Explainable Deep One-Class Classificationcited by 126
  12. 2018Attention Branch Network: Learning of Attention Mechanism for Visual Explanationcited by 108
  13. 2021Semantically Coherent Out-of-Distribution Detectioncited by 107
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  15. 2021MOOD: Multi-level Out-of-distribution Detectioncited by 94
  16. 2020A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AIcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 2.

9 papers.

  1. 2024Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimationcited by 17
  2. 2024Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detectioncited by 11
  3. 2023Out-of-Distribution Detection for Monocular Depth Estimationcited by 7
  4. 2025Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detectioncited by 3
  5. 2024FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive Learningcited by 2
  6. 2025Out-of-Distribution Detection in LiDAR Semantic Segmentation Using Epistemic Uncertainty from Hierarchical GMMscited by 2
  7. 2023MaskSearch: Querying Image Masks at Scalecited by 0
  8. 2026ALOOD: Exploiting Language Representations for LiDAR-based Out-of-Distribution Object Detectioncited by 0
  9. 2026Forecasting the Past: Gradient-Based Distribution Shift Detection in Trajectory Predictioncited by 0