mlsanityData to 7 Oct 2026

Hypercone Assisted Contour Generation for Out-of-Distribution Detection

arXiv 2501.10209 ↗First posted 17 Jan 2025cs.CV cs.LGcited by 1

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Abstract (arXiv)

Recent advances in the field of out-of-distribution (OOD) detection have placed great emphasis on learning better representations suited to this task. While there are distance-based approaches, distributional awareness has seldom been exploited for better performance. We present HACk-OOD, a novel OOD detection method that makes no distributional assumption about the data, but automatically adapts to its distribution. Specifically, HACk-OOD constructs a set of hypercones by maximizing the angular distance to neighbors in a given data-point's vicinity to approximate the contour within which in-distribution (ID) data-points lie. Experimental results show state-of-the-art FPR@95 and AUROC performance on Near-OOD detection and on Far-OOD detection on the challenging CIFAR-100 benchmark without explicitly training for OOD performance.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 47 references that are in the map. First posted on arXiv 17 Jan 2025.

Builds on

35 of 47 references.

References · 35

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23 more
  1. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  2. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 215
  3. 2022Extremely Simple Activation Shaping for Out-of-Distribution Detectioncited by 212
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  19. 2021Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detectioncited by 23
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  23. 2021Gradient-based Novelty Detection Boosted by Self-supervised Binary Classificationcited by 10

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Weldingcited by 0