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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