mlsanityData to 7 Oct 2026

Unsupervised Out-of-Distribution Detection with Diffusion Inpainting

arXiv 2302.10326 ↗First posted 20 Feb 2023cs.CV cs.LGcited by 57

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Unsupervised out-of-distribution detection (OOD) seeks to identify out-of-domain data by learning only from unlabeled in-domain data. We present a novel approach for this task - Lift, Map, Detect (LMD) - that leverages recent advancement in diffusion models. Diffusion models are one type of generative models. At their core, they learn an iterative denoising process that gradually maps a noisy image closer to their training manifolds. LMD leverages this intuition for OOD detection. Specifically, LMD lifts an image off its original manifold by corrupting it, and maps it towards the in-domain manifold with a diffusion model. For an out-of-domain image, the mapped image would have a large distance away from its original manifold, and LMD would identify it as OOD accordingly. We show through extensive experiments that LMD achieves competitive performance across a broad variety of datasets. Code can be found at https://github.com/zhenzhel/lift_map_detect.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 47 of its 64 references that are in the map. First posted on arXiv 20 Feb 2023.

Builds on

47 of 64 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Denoising diffusion models for out-of-distribution detection14 Nov

Other references · 46

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  6. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
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  8. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  9. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  10. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  11. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  12. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
34 more
  1. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  2. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  3. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  4. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  5. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
  6. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  7. 2021SimMIM: A Simple Framework for Masked Image Modelingcited by 1,147
  8. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  9. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  10. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 695
  11. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  12. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  13. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  14. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  15. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  16. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  17. 2018Deep Learning for Classical Japanese Literaturecited by 415
  18. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 404
  19. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 338
  20. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  21. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  22. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 220
  23. 2020Classification-Based Anomaly Detection for General Datacited by 210
  24. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  25. 2022Learning Fast Samplers for Diffusion Models by Differentiating Through Sample Qualitycited by 181
  26. 2020Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encodercited by 145
  27. 2019BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modelingcited by 109
  28. 2018Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distancecited by 74
  29. 2020Density of States Estimation for Out-of-Distribution Detectioncited by 74
  30. 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
  31. 2023A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Modelscited by 37
  32. 2019Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicalitycited by 30
  33. 2021Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?cited by 16
  34. 2022Model-agnostic out-of-distribution detection using combined statistical testscited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 23; 2025: 20; 2026: 9.

57 papers.

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  1. 2024AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Errorcited by 123
  2. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  3. 2024Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editingcited by 38
  4. 2024Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detectioncited by 30
  5. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 24
  6. 2024A Geometric Explanation of the Likelihood OOD Detection Paradoxcited by 22
  7. 2024Out-of-Distribution Detection with a Single Unconditional Diffusion Modelcited by 22
  8. 2024Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Modelscited by 21
  9. 2024Detecting Out-Of-Distribution Earth Observation Images with Diffusion Modelscited by 14
  10. 2025Out-of-Distribution Detection Methods Answer the Wrong Questionscited by 14
  11. 2024Towards Understanding and Quantifying Uncertainty for Text-to-Image Generationcited by 11
  12. 2023Likelihood-based Out-of-Distribution Detection with Denoising Diffusion Probabilistic Modelscited by 10
38 more
  1. 2023Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Modelscited by 9
  2. 2024Diffusion for Out-of-Distribution Detection on Road Scenes and Beyondcited by 9
  3. 2023DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifierscited by 5
  4. 2024Diffusion-based Layer-wise Semantic Reconstruction for Unsupervised Out-of-Distribution Detectioncited by 5
  5. 2025Building Machine Learning Challenges for Anomaly Detection in Sciencecited by 5
  6. 2025Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectivescited by 5
  7. 2024Feature Density Estimation for Out-of-Distribution Detection via Normalizing Flowscited by 4
  8. 2024Your Classifier Can Be Secretly a Likelihood-Based OOD Detectorcited by 4
  9. 2024Diffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detectioncited by 4
  10. 2025Unsupervised Detection of Distribution Shift in Inverse Problems using Diffusion Modelscited by 4
  11. 2025EigenScore: OOD Detection using Covariance in Diffusion Modelscited by 4
  12. 2025Can We Ignore Labels In Out of Distribution Detection?cited by 3
  13. 2025SCOPED: Score-Curvature Out-of-distribution Proximity Evaluator for Diffusioncited by 3
  14. 2023A Hybrid Architecture for Out of Domain Intent Detection and Intent Discoverycited by 2
  15. 2025Zero-Shot Image Anomaly Detection Using Generative Foundation Modelscited by 2
  16. 2026Geometry over Density: Few-Shot Cross-Domain OOD Detectioncited by 2
  17. 2024Detecting Out-of-Distribution Objects through Class-Conditioned Inpaintingcited by 1
  18. 2024Enhancing OOD Detection Using Latent Diffusioncited by 1
  19. 2025DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detectioncited by 1
  20. 2025OT-DETECTOR: Delving into Optimal Transport for Zero-shot Out-of-Distribution Detectioncited by 1
  21. 2025Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximationcited by 1
  22. 2025Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutionscited by 1
  23. 2026GEPC: Group-Equivariant Posterior Consistency for Out-of-Distribution Detection in Diffusion Modelscited by 1
  24. 2026Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imagingcited by 1
  25. 2024Rethinking Test-time Likelihood: The Likelihood Path Principle and Its Application to OOD Detectioncited by 0
  26. 2024Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosiscited by 0
  27. 2024Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposurecited by 0
  28. 2024Localizing Anomalies via Multiscale Score Matching Analysiscited by 0
  29. 2024Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detectioncited by 0
  30. 2024Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Modelcited by 0
  31. 2024Exploring Out-of-distribution Detection for Sparse-view Computed Tomography with Diffusion Modelscited by 0
  32. 2025Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Maskingcited by 0
  33. 2025DisPatch: Disarming Adversarial Patches in Object Detection with Diffusion Modelscited by 0
  34. 2025Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detectioncited by 0
  35. 2025Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectoriescited by 0
  36. 2025Bayesian model selection and misspecification testing in imaging inverse problems only from noisy and partial measurementscited by 0
  37. 2025Perspectives on a Reliability Monitoring Framework for Agentic AI Systemscited by 0
  38. 2025Diffusion Reconstruction-based Data Likelihood Estimation for Core-Set Selectioncited by 0

50 most cited of 57 All 57 (JSON)

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  1. 2024NODI: Out-Of-Distribution Detection with Noise from Diffusion
  2. 2025GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detection
  3. 2024Exploiting Diffusion Prior for Out-of-Distribution Detection
  4. 2025DOSE3 : Diffusion-based Out-of-distribution detection on SE(3) trajectories
  5. 2025Probability Density from Latent Diffusion Models for Out-of-Distribution Detection
  6. 2023Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
  7. 2025Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories
  8. 2024Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection