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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.
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