Abstract
Out-of-distribution (OOD) detection is crucial for real-world machine learning. Outlier exposure methods, which use auxiliary outlier data, can significantly enhance OOD detection. We present Hopfield Boosting, a boosting technique employing modern Hopfield energy (MHE) to refine the boundary between in-distribution (ID) and OOD data. Our method focuses on challenging outlier examples near the decision boundary, achieving a 40% improvement in FPR95 on CIFAR-10, setting a new OOD detection state-of-the-art with outlier exposure.
Originalsprache | Englisch |
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Titel | NeurIPS 2023 Workshop |
Untertitel | Associative Memory & Hopfield Networks |
Publikationsstatus | Angenommen/Im Druck - 15 Dez. 2023 |