Poster
in
Workshop: Associative Memory & Hopfield Networks in 2023
Hopfield Boosting for Out-of-Distribution Detection
Claus Hofmann · Simon Schmid · Bernhard Lehner · Daniel Klotz · Sepp Hochreiter
[
Abstract
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[ Project Page ]
presentation:
Associative Memory & Hopfield Networks in 2023
Fri 15 Dec 6:15 a.m. PST — 3:30 p.m. PST
[
OpenReview]
Fri 15 Dec 6:15 a.m. PST — 3:30 p.m. PST
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.
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