mlsanityData to 7 Oct 2026

Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Models

arXiv 2310.17432 ↗First posted 26 Oct 2023cs.LGcited by 10

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Out-of-Distribution detection between dataset pairs has been extensively explored with generative models. We show that likelihood-based Out-of-Distribution detection can be extended to diffusion models by leveraging the fact that they, like other likelihood-based generative models, are dramatically affected by the input sample complexity. Currently, all Out-of-Distribution detection methods with Diffusion Models are reconstruction-based. We propose a new likelihood ratio for Out-of-Distribution detection with Deep Denoising Diffusion Models, which we call the Complexity Corrected Likelihood Ratio. Our likelihood ratio is constructed using Evidence Lower-Bound evaluations from an individual model at various noising levels. We present results that are comparable to state-of-the-art Out-of-Distribution detection methods with generative models.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 28 of its 38 references that are in the map. First posted on arXiv 26 Oct 2023.

Builds on

28 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpainting20 Feb
  2. 2022Denoising diffusion models for out-of-distribution detection14 Nov

Other references · 26

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  7. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  8. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  9. 2021Variational Diffusion Modelscited by 1,145
  10. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  11. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  12. 2020WaveGrad: Estimating Gradients for Waveform Generationcited by 620
14 more
  1. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  2. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  3. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  4. 2023Simple diffusion: End-to-end diffusion for high resolution imagescited by 413
  5. 2022Cold Diffusion: Inverting Arbitrary Image Transforms Without Noisecited by 275
  6. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  7. 2020Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encodercited by 145
  8. 2018Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distancecited by 74
  9. 2020Density of States Estimation for Out-of-Distribution Detectioncited by 74
  10. 2021Hierarchical VAEs Know What They Don't Knowcited by 58
  11. 2020Multiscale Score Matching for Out-of-Distribution Detectioncited by 36
  12. 2021On the Out-of-distribution Generalization of Probabilistic Image Modellingcited by 32
  13. 2020Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimationcited by 24
  14. 2020Probabilistic Autoencodercited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 3; 2026: 2.

10 papers.

  1. 2024A Geometric Explanation of the Likelihood OOD Detection Paradoxcited by 22
  2. 2024Detecting Out-Of-Distribution Earth Observation Images with Diffusion Modelscited by 14
  3. 2024Diffusion for Out-of-Distribution Detection on Road Scenes and Beyondcited by 9
  4. 2025Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representationscited by 6
  5. 2025Probability Density from Latent Diffusion Models for Out-of-Distribution Detectioncited by 2
  6. 2024Differentiable Score-Based Likelihoods: Learning CT Motion Compensation From Clean Imagescited by 1
  7. 2024Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detectioncited by 0
  8. 2025Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphscited by 0
  9. 2026Generalization and Memorization in Rectified Flowcited by 0
  10. 2026Backbone-Equated Diffusion OOD via Sparse Internal Snapshotscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection
  2. 2025Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories
  3. 2025Diffusion Models: A Mathematical Introduction
  4. 2023Unsupervised 3D out-of-distribution detection with latent diffusion models
  5. 2022From Denoising Diffusions to Denoising Markov Models
  6. 2024Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detection
  7. 2025Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds
  8. 2022Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection