Energy Correction Model in the Feature Space for Out-of-Distribution
  Detection
Marc Lafon Clément Rambour Nicolas Thome
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 an energy-based correction of a mixture of
class-conditional Gaussian distributions. We obtains favorable results when
compared to a strong baseline like the KNN detector on the CIFAR-10/CIFAR-100
OOD detection benchmarks.