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Title: WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis
Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance of detecting out-of-distribution (OOD) samples in the open world. In this work, we propose a novel feature-space OOD detection score based on class-specific and class-agnostic information. Specifically, the approach utilizes Whitened Linear Discriminant Analysis to project features into two subspaces the discriminative and residual subspaces - for which the in-distribution (ID) classes are maximally separated and closely clustered, respectively. The OOD score is then determined by combining the deviation from the input data to the ID pattern in both subspaces. The efficacy of our method, named WDiscOOD, is verified on the large-scale ImageNet-1k benchmark, with six OOD datasets that cover a variety of distribution shifts. WDiscOOD demonstrates superior performance on deep classifiers with diverse backbone architectures, including CNN and vision transformer. Furthermore, we also show that WDiscOOD more effectively detects novel concepts in representation spaces trained with contrastive objectives, including supervised contrastive loss and multi-modality contrastive loss.  more » « less
Award ID(s):
2026611
PAR ID:
10545740
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-0718-4
Page Range / eLocation ID:
5275 to 5284
Subject(s) / Keyword(s):
Out-of-Distribution Detection, Deep Learning
Format(s):
Medium: X
Location:
Paris, France
Sponsoring Org:
National Science Foundation
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