NECO: NEural Collapse Based Out-of-distribution detection
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866917598774427648 |
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| author | Ammar, Mouïn Ben Belkhir, Nacim Popescu, Sebastian Manzanera, Antoine Franchi, Gianni |
| author_facet | Ammar, Mouïn Ben Belkhir, Nacim Popescu, Sebastian Manzanera, Antoine Franchi, Gianni |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06823 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NECO: NEural Collapse Based Out-of-distribution detection Ammar, Mouïn Ben Belkhir, Nacim Popescu, Sebastian Manzanera, Antoine Franchi, Gianni Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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 |
| title | NECO: NEural Collapse Based Out-of-distribution detection |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.06823 |