mlsanityData to 14 Oct 2026

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

arXiv 2305.16966 ↗First posted 26 May 2023cs.CVcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 42 of its 62 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

42 of 62 references.

References · 42

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  6. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  7. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  8. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  9. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  10. 2013Describing Textures in the Wildcited by 2,125
  11. 2016Concrete Problems in AI Safetycited by 1,642
  12. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
30 more
  1. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
  2. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  3. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  4. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  5. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  6. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  7. 2015Towards Open Set Deep Networkscited by 538
  8. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  9. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 405
  10. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
  11. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  12. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 339
  13. 2021How to Train Your Energy-Based Modelscited by 274
  14. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  15. 2014Towards Open World Recognitioncited by 226
  16. 2020Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Predictioncited by 195
  17. 2017Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Artcited by 182
  18. 2016A Theory of Generative ConvNetcited by 174
  19. 2021DICE: Leveraging Sparsification for Out-of-Distribution Detectioncited by 166
  20. 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 164
  21. 2019TossingBot: Learning to Throw Arbitrary Objects with Residual Physicscited by 137
  22. 2020Learning Latent Space Energy-Based Prior Modelcited by 104
  23. 2020VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Modelscited by 104
  24. 2020Augmenting Physical Models with Deep Networks for Complex Dynamics Forecastingcited by 95
  25. 2021Unsupervised Learning of Compositional Energy Conceptscited by 71
  26. 2022A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Modelcited by 44
  27. 2020Learning Energy-Based Model with Variational Auto-Encoder as Amortized Samplercited by 43
  28. 2020MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMCcited by 26
  29. 2021On Out-of-distribution Detection with Energy-based Modelscited by 17
  30. 2022Complementing Brightness Constancy with Deep Networks for Optical Flow Predictioncited by 1

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 5; 2025: 2.

9 papers.

  1. 2024GalLoP: Learning Global and Local Prompts for Vision-Language Modelscited by 29
  2. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  3. 2024Rethinking Out-of-Distribution Detection on Imbalanced Data Distributioncited by 6
  4. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  5. 2023FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Scorecited by 5
  6. 2024Your Classifier Can Be Secretly a Likelihood-Based OOD Detectorcited by 4
  7. 2025Incorporating Inductive Biases to Energy-based Generative Modelscited by 1
  8. 2023Distilling the Unknown to Unveil Certaintycited by 0
  9. 2024LIME-Eval: Rethinking Low-light Image Enhancement Evaluation via Object Detectioncited by 0