Poster
in
Workshop: Workshop on Machine Learning Safety

HEAT: Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection

Marc Lafon · ClĂ©ment Rambour · Nicolas THOME


Abstract:

In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier. We show that learning the density of in-distribution (ID) features with an energy-based models (EBM) leads to competitive detection results. However, we found that the non-mixing of MCMC sampling during the EBM's training undermines its detection performance. To overcome this, we introduce HEAT, an energy-based correction of a mixture of class-conditional Gaussian distributions. We show that HEAT obtains favorable results when compared to a strong baseline like the KNN detector on the CIFAR-10/CIFAR-100 OOD detection benchmarks.

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