NECO: NEural Collapse Based Out-of-distribution detection

• URL: http://arxiv.org/abs/2310.06823v3
• Date: Tue, 27 Feb 2024 15:33:52 GMT
• Title: NECO: NEural Collapse Based Out-of-distribution detection
• Authors: Mou\"in Ben Ammar, Nacim Belkhir, Sebastian Popescu, Antoine Manzanera, Gianni Franchi
• Abstract summary: We introduce NECO, a novel post-hoc method for OOD detection. Our experiments demonstrate that NECO achieves both small and large-scale OOD detection tasks. We provide a theoretical explanation for the effectiveness of our method in OOD detection.
• Score: 2.4958897155282282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detecting out-of-distribution (OOD) data is a critical challenge in machine learning due to model overconfidence, often without awareness of their epistemological limits. We hypothesize that ``neural collapse'', a phenomenon affecting in-distribution data for models trained beyond loss convergence, also influences OOD data. To benefit from this interplay, we introduce NECO, a novel post-hoc method for OOD detection, which leverages the geometric properties of ``neural collapse'' and of principal component spaces to identify OOD data. Our extensive experiments demonstrate that NECO achieves state-of-the-art results on both small and large-scale OOD detection tasks while exhibiting strong generalization capabilities across different network architectures. Furthermore, we provide a theoretical explanation for the effectiveness of our method in OOD detection. Code is available at https://gitlab.com/drti/neco

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