Energy Correction Model in the Feature Space for Out-of-Distribution Detection
<|reference_start|>Energy Correction Model in the Feature Space for Out-of-Distribution Detection: 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.<|reference_end|>
@article{lafon2024energy,
    title={Energy Correction Model in the Feature Space for Out-of-Distribution
  Detection},
    author={Marc Lafon and Cl'ement Rambour and Nicolas Thome},
    journal={arXiv preprint arXiv:2403.10403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10403},
    primaryClass={cs.CV cs.AI cs.LG}
}