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Energy Correction Model in the Feature Space for Out-of-Distribution Detection

arXiv 2403.10403 ↗First posted 15 Mar 2024cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 15 Mar 2024.

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18 of 29 references.

References · 18

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  3. 2013Describing Textures in the Wildcited by 2,110
  4. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  5. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  6. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  7. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  8. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  9. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 402
  10. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  11. 2021How to Train Your Energy-Based Modelscited by 271
  12. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
6 more
  1. 2016A Theory of Generative ConvNetcited by 174
  2. 2020Learning Energy-Based Models by Diffusion Recovery Likelihoodcited by 122
  3. 2019Flow Contrastive Estimation of Energy-Based Modelscited by 75
  4. 2020Generalized Energy Based Modelscited by 73
  5. 2020MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMCcited by 25
  6. 2014Generative Modeling of Convolutional Neural Networkscited by 5

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