Hopfield Boosting for Out-of-Distribution Detection

Published: 27 Oct 2023, Last Modified: 26 Nov 2023AMHN23 OralEveryoneRevisionsBibTeX
Keywords: hopfield networks, hopfield energy, ood, out of distribution, boosting, maximum margin, deep learning
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.
Submission Number: 42