mlsanityData to 14 Oct 2026

Dream the Impossible: Outlier Imagination with Diffusion Models

arXiv 2309.13415 ↗First posted 23 Sep 2023cs.LG cs.CVcited by 77

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Utilizing auxiliary outlier datasets to regularize the machine learning model has demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the labor intensity in data collection and cleaning, automating outlier data generation has been a long-desired alternative. Despite the appeal, generating photo-realistic outliers in the high dimensional pixel space has been an open challenge for the field. To tackle the problem, this paper proposes a new framework DREAM-OOD, which enables imagining photo-realistic outliers by way of diffusion models, provided with only the in-distribution (ID) data and classes. Specifically, DREAM-OOD learns a text-conditioned latent space based on ID data, and then samples outliers in the low-likelihood region via the latent, which can be decoded into images by the diffusion model. Different from prior works, DREAM-OOD enables visualizing and understanding the imagined outliers, directly in the pixel space. We conduct comprehensive quantitative and qualitative studies to understand the efficacy of DREAM-OOD, and show that training with the samples generated by DREAM-OOD can benefit OOD detection performance. Code is publicly available at https://github.com/deeplearning-wisc/dream-ood.

Key facts. Cited by 77 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 111 of its 134 references that are in the map. First posted on arXiv 23 Sep 2023.

Builds on

111 of 134 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023Unsupervised Out-of-Distribution Detection with Diffusion Inpainting20 Feb
  2. 2022Improving dermatology classifiers across populations using images generated by large diffusion models23 Nov
  3. 2022Denoising diffusion models for out-of-distribution detection14 Nov
  4. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models2 Nov
  5. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep
  6. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  7. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 104

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2020Denoising Diffusion Implicit Modelscited by 9,781
  6. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  8. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  9. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  10. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  11. 2022Scalable Diffusion Models with Transformerscited by 6,054
  12. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
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  1. 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  3. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  4. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  5. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  6. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  7. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,884
  8. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  9. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  10. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  11. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  12. 2013Describing Textures in the Wildcited by 2,125
  13. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,979
  14. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,850
  15. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,778
  16. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,515
  17. 2021Image Super-Resolution via Iterative Refinementcited by 1,511
  18. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  19. 2019Natural Adversarial Examplescited by 1,408
  20. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
  21. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  22. 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
  23. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  24. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
  25. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
  26. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  27. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  28. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  29. 2022Pseudo Numerical Methods for Diffusion Models on Manifoldscited by 700
  30. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 672
  31. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  32. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  33. 2018Predictive Uncertainty Estimation via Prior Networkscited by 557
  34. 2015Towards Open Set Deep Networkscited by 538
  35. 2021ReAct: Out-of-distribution Detection With Rectified Activationscited by 486
  36. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  37. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  38. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405

50 most cited of 104 All 104 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 31; 2025: 33; 2026: 10.

77 papers.

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  1. 2024ARGS: Alignment as Reward-Guided Searchcited by 103
  2. 2024Process Reward Model with Q-Value Rankingscited by 80
  3. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  4. 2025Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentationcited by 23
  5. 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learningcited by 22
  6. 2024ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Objectcited by 22
  7. 2024Self-Guided Generation of Minority Samples Using Diffusion Modelscited by 19
  8. 2023TP2O: Creative Text Pair-to-Object Generation using Balance Swap-Samplingcited by 16
  9. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  10. 2024Placing Objects in Context via Inpainting for Out-of-distribution Segmentationcited by 12
  11. 2024Can OOD Object Detectors Learn from Foundation Models?cited by 10
  12. 2024FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detectorcited by 10
38 more
  1. 2024Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Modelscited by 9
  2. 2024On the Learnability of Out-of-distribution Detectioncited by 8
  3. 2025PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomaliescited by 8
  4. 2024GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networkscited by 7
  5. 2025RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samplescited by 7
  6. 2025AdaSCALE: Adaptive Scaling for OOD Detectioncited by 7
  7. 2024When and How Does In-Distribution Label Help Out-of-Distribution Detection?cited by 6
  8. 2024Out-of-Distribution Learning with Human Feedbackcited by 6
  9. 2024Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddingscited by 6
  10. 2024DFA-RAG: Conversational Semantic Router for Large Language Model with Definite Finite Automatoncited by 5
  11. 2025Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generationcited by 5
  12. 2025Sample-aware RandAugment: Search-free Automatic Data Augmentation for Effective Image Recognitioncited by 5
  13. 2025Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detectioncited by 5
  14. 2025GOOD: Training-Free Guided Diffusion Sampling for Out-of-Distribution Detectioncited by 5
  15. 2024Future-Proofing Class-Incremental Learningcited by 4
  16. 2024Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Datacited by 4
  17. 2024Diffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detectioncited by 4
  18. 2024Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similaritycited by 4
  19. 2024What If the Input is Expanded in OOD Detection?cited by 4
  20. 2025Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detectioncited by 4
  21. 2025Out-of-Distribution Detection using Synthetic Data Generationcited by 4
  22. 2025BOOD: Boundary-based Out-Of-Distribution Data Generationcited by 4
  23. 2024PViT: Prior-augmented Vision Transformer for Out-of-distribution Detectioncited by 3
  24. 2025Mitigating the Modality Gap: Few-Shot Out-of-Distribution Detection with Multi-modal Prototypes and Image Bias Estimationcited by 3
  25. 2025Generative Data Mining with Longtail-Guided Diffusioncited by 3
  26. 2025Enhancing Out-of-Distribution Detection with Extended Logit Normalizationcited by 3
  27. 2025Harmonizing Geometry and Uncertainty: Diffusion with Hyperspherescited by 3
  28. 2025FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detectioncited by 3
  29. 2025Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detectioncited by 3
  30. 2025Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networkscited by 3
  31. 2024Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generationcited by 2
  32. 2024FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detectioncited by 2
  33. 2025Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimizationcited by 2
  34. 2025Medix: Out-of-Distribution Detection from Unlabeled Wild Data via Robust Gradient Statisticscited by 2
  35. 2023RankFeat&RankWeight: Rank-1 Feature/Weight Removal for Out-of-distribution Detectioncited by 1
  36. 2024Detecting Out-of-Distribution Objects through Class-Conditioned Inpaintingcited by 1
  37. 2024How Out-of-Distribution Detection Learning Theory Enhances Transformer: Learnability and Reliabilitycited by 1
  38. 2024Enhancing OOD Detection Using Latent Diffusioncited by 1

50 most cited of 77 All 77 (JSON)

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  1. 2023On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection
  2. 2024Non-Linear Outlier Synthesis for Out-of-Distribution Detection
  3. 2025Probability Density from Latent Diffusion Models for Out-of-Distribution Detection
  4. 2025Zero-Shot Image Anomaly Detection Using Generative Foundation Models
  5. 2025Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations
  6. 2025Beyond Binary Out-of-Distribution Detection: Characterizing Distributional Shifts with Multi-Statistic Diffusion Trajectories
  7. 2024Exploiting Diffusion Prior for Out-of-Distribution Detection
  8. 2024NODI: Out-Of-Distribution Detection with Noise from Diffusion