Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
Abstract
Out-of-distribution (OOD) detection is a critical requirement for the deployment of deep neural networks. This paper introduces the HEAT model, a new post-hoc OOD detection method estimating the density of in-distribution (ID) samples using hybrid energy-based models (EBM) in the feature space of a pre-trained backbone. HEAT complements prior density estimators of the ID density, e.g. parametric models like the Gaussian Mixture Model (GMM), to provide an accurate yet robust density estimation. A second contribution is to leverage the EBM framework to provide a unified density estimation and to compose several energy terms. Extensive experiments demonstrate the significance of the two contributions. HEAT sets new state-of-the-art OOD detection results on the CIFAR-10 / CIFAR-100 benchmark as well as on the large-scale Imagenet benchmark. The code is available at: https://github.com/MarcLafon/heatood.
Keywords
Cite
@article{arxiv.2305.16966,
title = {Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection},
author = {Marc Lafon and Elias Ramzi and Clément Rambour and Nicolas Thome},
journal= {arXiv preprint arXiv:2305.16966},
year = {2023}
}