mlsanityData to 7 Oct 2026

EOOD: Entropy-based Out-of-distribution Detection

arXiv 2504.03342 ↗First posted 4 Apr 2025cs.CV cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Deep neural networks (DNNs) often exhibit overconfidence when encountering out-of-distribution (OOD) samples, posing significant challenges for deployment. Since DNNs are trained on in-distribution (ID) datasets, the information flow of ID samples through DNNs inevitably differs from that of OOD samples. In this paper, we propose an Entropy-based Out-Of-distribution Detection (EOOD) framework. EOOD first identifies specific block where the information flow differences between ID and OOD samples are more pronounced, using both ID and pseudo-OOD samples. It then calculates the conditional entropy on the selected block as the OOD confidence score. Comprehensive experiments conducted across various ID and OOD settings demonstrate the effectiveness of EOOD in OOD detection and its superiority over state-of-the-art methods.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 34 of its 59 references that are in the map. First posted on arXiv 4 Apr 2025.

Builds on

34 of 59 references.

References · 34

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22 more
  1. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  2. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  3. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
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  15. 2022Boosting Out-of-distribution Detection with Typical Featurescited by 48
  16. 2023Out-of-distribution Detection with Implicit Outlier Transformationcited by 42
  17. 2023Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolationcited by 38
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspectivecited by 8
  2. 2026CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoningcited by 3
  3. 2026Hierarchical Semantic-Augmented Navigation: Optimal Transport and Graph-Driven Reasoning for Vision-Language Navigationcited by 1
  4. 2026Optimal Transport-Induced Samples against Out-of-Distribution Overconfidencecited by 0
  5. 2026Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detectioncited by 0