mlsanityData to 7 Oct 2026

Non-Parametric Outlier Synthesis

arXiv 2303.02966 ↗First posted 6 Mar 2023cs.LG cs.AIcited by 129

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Abstract (arXiv)

Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Recent work on outlier synthesis modeled the feature space as parametric Gaussian distribution, a strong and restrictive assumption that might not hold in reality. In this paper, we propose a novel framework, Non-Parametric Outlier Synthesis (NPOS), which generates artificial OOD training data and facilitates learning a reliable decision boundary between ID and OOD data. Importantly, our proposed synthesis approach does not make any distributional assumption on the ID embeddings, thereby offering strong flexibility and generality. We show that our synthesis approach can be mathematically interpreted as a rejection sampling framework. Extensive experiments show that NPOS can achieve superior OOD detection performance, outperforming the competitive rivals by a significant margin. Code is publicly available at https://github.com/deeplearning-wisc/npos.

Key facts. Cited by 129 papers in the map (data through 7 Oct 2026). Builds on 48 of its 62 references that are in the map. First posted on arXiv 6 Mar 2023.

Builds on

48 of 62 references.

References · 48

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2017On Calibration of Modern Neural Networkscited by 4,860
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  6. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  7. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  8. 2013Describing Textures in the Wildcited by 2,110
  9. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  10. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,350
  11. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,325
  12. 2020Energy-based Out-of-distribution Detectioncited by 1,296
36 more
  1. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  2. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  3. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  4. 2018Predictive Uncertainty Estimation via Prior Networkscited by 555
  5. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 555
  6. 2015Towards Open Set Deep Networkscited by 533
  7. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  8. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  9. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 404
  10. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  11. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  12. 2020BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learningcited by 368
  13. 2021On the Importance of Gradients for Detecting Distributional Shifts in the Wildcited by 338
  14. 2019SelectiveNet: A Deep Neural Network with an Integrated Reject Optioncited by 333
  15. 2018Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problemcited by 323
  16. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  17. 2021Exploring the Limits of Out-of-Distribution Detectioncited by 283
  18. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  19. 2022Mitigating Neural Network Overconfidence with Logit Normalizationcited by 251
  20. 2021MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Spacecited by 233
  21. 2020Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networkscited by 227
  22. 2014Towards Open World Recognitioncited by 226
  23. 2022Delving into Out-of-Distribution Detection with Vision-Language Representationscited by 215
  24. 2015TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Trackingcited by 165
  25. 2022How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?cited by 128
  26. 2022POEM: Out-of-Distribution Detection with Posterior Samplingcited by 108
  27. 2021Semantically Coherent Out-of-Distribution Detectioncited by 107
  28. 2019Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustnesscited by 104
  29. 2021Can multi-label classification networks know what they don't know?cited by 100
  30. 2022Training OOD Detectors in their Natural Habitatscited by 86
  31. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  32. 2019Towards neural networks that provably know when they don't knowcited by 69
  33. 2020MoPro: Webly Supervised Learning with Momentum Prototypescited by 66
  34. 2018Discriminative out-of-distribution detection for semantic segmentationcited by 47
  35. 2018Directional Statistics-based Deep Metric Learning for Image Classification and Retrievalcited by 16
  36. 2015Density Level Sets: Asymptotics, Inference, and Visualizationcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 28; 2024: 44; 2025: 40; 2026: 17.

129 papers.

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  1. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 178
  2. 2023LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learningcited by 107
  3. 2024ARGS: Alignment as Reward-Guided Searchcited by 99
  4. 2024Process Reward Model with Q-Value Rankingscited by 78
  5. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  6. 2024Negative Label Guided OOD Detection with Pretrained Vision-Language Modelscited by 66
  7. 2025Can We Detect Failures Without Failure Data? Uncertainty-Aware Runtime Failure Detection for Imitation Learning Policiescited by 49
  8. 2023Fast Decision Boundary based Out-of-Distribution Detectorcited by 48
  9. 2024Learning with Mixture of Prototypes for Out-of-Distribution Detectioncited by 43
  10. 2023ID-like Prompt Learning for Few-Shot Out-of-Distribution Detectioncited by 42
  11. 2023Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolationcited by 38
  12. 2023Learning to Augment Distributions for Out-of-Distribution Detectioncited by 38
38 more
  1. 2023GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detectioncited by 36
  2. 2024How Does Unlabeled Data Provably Help Out-of-Distribution Detection?cited by 36
  3. 2024Outlier detection by ensembling uncertainty with negative objectnesscited by 34
  4. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  5. 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sourcescited by 31
  6. 2024MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalitiescited by 28
  7. 2023DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalizationcited by 24
  8. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 24
  9. 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learningcited by 22
  10. 2024LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language Modelscited by 21
  11. 2024OVOR: OnePrompt with Virtual Outlier Regularization for Rehearsal-Free Class-Incremental Learningcited by 20
  12. 2024Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalizationcited by 17
  13. 2024AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Modelscited by 16
  14. 2025Reliable Few-shot Learning under Dual Noisescited by 16
  15. 2024Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detectioncited by 15
  16. 2025GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generationcited by 15
  17. 2023NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generationcited by 14
  18. 2024Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detectioncited by 14
  19. 2024CLIPScope: Enhancing Zero-Shot OOD Detection with Bayesian Scoringcited by 13
  20. 2024Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Modelscited by 13
  21. 2023AUTO: Adaptive Outlier Optimization for Test-Time OOD Detectioncited by 12
  22. 2023On the Powerfulness of Textual Outlier Exposure for Visual OoD Detectioncited by 12
  23. 2023Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognitioncited by 11
  24. 2024Can OOD Object Detectors Learn from Foundation Models?cited by 10
  25. 2025Compositional Generalization from Learned Skills via CoT Training: A Theoretical and Structural Analysis for Reasoningcited by 10
  26. 2023Activate and Reject: Towards Safe Domain Generalization under Category Shiftcited by 9
  27. 2023Model-free Test Time Adaptation for Out-Of-Distribution Detectioncited by 9
  28. 2024Energy-based Hopfield Boosting for Out-of-Distribution Detectioncited by 9
  29. 2024SeTAR: Out-of-Distribution Detection with Selective Low-Rank Approximationcited by 9
  30. 2023Conservative Prediction via Data-Driven Confidence Minimizationcited by 8
  31. 2024On the Learnability of Out-of-distribution Detectioncited by 8
  32. 2025Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentationcited by 8
  33. 2025Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detectioncited by 8
  34. 2024OpenCIL: Benchmarking Out-of-Distribution Detection in Class-Incremental Learningcited by 7
  35. 2025RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samplescited by 7
  36. 2025AdaSCALE: Adaptive Scaling for OOD Detectioncited by 7
  37. 2024CLIP-driven Outliers Synthesis for few-shot OOD detectioncited by 6
  38. 2024When and How Does In-Distribution Label Help Out-of-Distribution Detection?cited by 6

50 most cited of 129 All 129 (JSON)