This repo aims to provide the most comprehensive, up-to-date, high-quality resource for OOD detection, robustness, and generalization in Deep Learning. If you spot errors or omissions, please open an issue or contact me at continuousml@gmail.com.
(2020) Out-of-Distribution Detection in Deep Neural Networks by Neeraj Varshney
(2022) Anomaly detection for OOD and novel category detection by Thomas G. Dietterich
(2022) Reliable Open-World Learning Against Out-of-distribution Data by Sharon Yixuan Li
(2022) Challenges and Opportunities in Out-of-distribution Detection by Sharon Yixuan Li
(2022) Exploring the limits of out-of-distribution detection in vision and biomedical applications by Jie Ren
(2021) Understanding the Failure Modes of Out-of-distribution Generalization by Vaishnavh Nagarajan
(2020) Uncertainty and Out-of-Distribution Robustness in Deep Learning by Balaji Lakshminarayanan, Dustin Tran, and Jasper Snoek
OpenOOD: Benchmarking Generalized OOD Detection
PyTorch Out-of-Distribution Detection
Generalized Out-of-Distribution Detection: A Survey by Yang et al
A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges by Salehi et al.
"Know thy literature"
(ArXiv 2023) Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization by Liu et al.
(ArXiv 2023) Characterizing Out-of-Distribution Error via Optimal Transport by Lu et al.
(CVPR 2023) Distribution Shift Inversion for Out-of-Distribution Prediction [Code] by Yu et al.
(CVPR 2023) Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection [Code] by Lu et al.
(CVPR 2023) GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection [Video] [Code] by Liu et al.
(CVPR 2023) (NAP) Detection of Out-of-Distribution Samples Using Binary Neuron Activation Patterns [Code] by Olber et al.
(CVPR 2023) Decoupling MaxLogit for Out-of-Distribution Detection by Zhang and Xiang
(CVPR 2023) Balanced Energy Regularization Loss for Out-of-Distribution Detection [Code] by Choi et al.
(CVPR 2023) Rethinking Out-of-Distribution (OOD) Detection: Masked Image Modeling Is All You Need [Code] by Li et al.
(CVPR 2023) LINe: Out-of-Distribution Detection by Leveraging Important Neurons [Code] by Ahn et al.
(ICLR 2023) ⭐⭐⭐⭐⭐ A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet [Code] by Galil et al.
(ICLR 2023) Energy-based Out-of-Distribution Detection for Graph Neural Networks [Code] by Wu et al.
(ICLR 2023) The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection [Code] by Floto et al.
(ICLR 2023) Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy by Zhang et al.
(ICLR 2023) Out-of-Distribution Detection and Selective Generation for Conditional Language Models by Ren et al.
(ICLR 2023) Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection by Yu et al.
(ICLR 2023) Non-Parametric Outlier Synthesis [Code] by Tao et al.
(ICLR 2023) Out-of-distribution Detection with Implicit Outlier Transformation by Wang et al.
(ICML 2023) Unsupervised Out-of-Distribution Detection with Diffusion Inpainting by Liu et al.
(ICML 2023) Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting by Bagi et al.
(ICML 2023) Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization [Code] by Ramé et al.
(ICML 2023) Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships by Guo et al.
(ICML 2023) Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection [Video] by Bai et al.
(ICML 2023) Concept-based Explanations for Out-of-Distribution Detectors by Choi et al.
(ICML 2023) Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection by Lafon et al.
(ICML 2023) Detecting Out-of-distribution Data through In-distribution Class Prior by Jiang et al.
(ICML 2023) Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [Code] by Zhu et al
(ICML 2023) In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation [Code] by Bitterwolf et al.
(AAAI 2023) READ: Aggregating Reconstruction Error into Out-of-Distribution Detection by Jiang et al.
(AAAI 2023) Towards In-Distribution Compatible Out-of-Distribution Detection by Wu et al.
(TMLR 2022) Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks by Haas et al.
(CVPR 2022) ViM: Out-Of-Distribution with Virtual-logit Matching [Project Page] by Wang et al.
(CVPR 2022) Neural Mean Discrepancy for Efficient Out-of-Distribution Detection by Dong et al.
(CVPR 2022) Deep Hybrid Models for Out-of-Distribution Detection by Cao and Zhang
(CVPR 2022) Rethinking Reconstruction Autoencoder-Based Out-of-Distribution Detection by Yibo Zhou
(CVPR 2022) Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wild [Code] by Du et al.
(NeurIPS 2022) ⭐⭐⭐⭐⭐ OpenOOD: Benchmarking Generalized Out-of-Distribution Detection [Code] by Yang et al.
(NeurIPS 2022) Boosting Out-of-distribution Detection with Typical Features by Zhu et al.
(NeurIPS 2022) GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs [Code] by Li et al.
(NeurIPS 2022) Out-of-Distribution Detection via Conditional Kernel Independence Model by Wang et al.
(NeurIPS 2022) Your Out-of-Distribution Detection Method is Not Robust! [Code] by Azizmalayeri et al.
(NeurIPS 2022) Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE by Li et al.
(NeurIPS 2022) GOOD: A Graph Out-of-Distribution Benchmark [Code] by Gui et al.
(NeurIPS 2022) ⭐⭐⭐⭐⭐ Is Out-of-Distribution Detection Learnable? by Fang et al.
(NeurIPS 2022) Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment by Yang et al.
(NeurIPS 2022) Delving into Out-of-Distribution Detection with Vision-Language Representations [Video] [Code] by Ming et al.
(NeurIPS 2022) Beyond Mahalanobis Distance for Textual OOD Detection by Colombo et al.
(NeurIPS 2022) Density-driven Regularization for Out-of-distribution Detection by Huang et al.
(NeurIPS 2022) SIREN: Shaping Representations for Detecting Out-of-Distribution Objects [Code] by Du et al.
(ICML 2022) Mitigating Neural Network Overconfidence with Logit Normalization [Code] by Hsu et al.
(ICML 2022) Scaling Out-of-Distribution Detection for Real-World Settings [Code] by Hendrycks et al.
(ICML 2022) Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets by Wei et al.
(ICML 2022) Model Agnostic Sample Reweighting for Out-of-Distribution Learning [Code] by Zhou et al.
(ICML 2022) Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition [Code] by Wang et al.
(ICML 2022) Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities [Code] by Bitterwolf et al.
(ICML 2022) Predicting Out-of-Distribution Error with the Projection Norm [Code] by Yu et al.
(ICML 2022) POEM: Out-of-Distribution Detection with Posterior Sampling [Code] by Ming et al.
(ICML 2022) (kNN) Out-of-Distribution Detection with Deep Nearest Neighbors [Code] by Sun et al.
(ICML 2022) Training OOD Detectors in their Natural Habitats by Katz-Samuels et al.
(ICLR 2022) Extremely Simple Activation Shaping for Out-of-Distribution Detection [Code] by Djurisic et al.
(ICLR 2022) Revisiting flow generative models for Out-of-distribution detection by Jiang et al.
(ICLR 2022) PI3NN: Out-of-distribution-aware Prediction Intervals from Three Neural Networks [Code] by Liu et al.
(ICLR 2022) (ATC) Leveraging unlabeled data to predict out-of-distribution performance by Garg et al.
(ICLR 2022) Igeood: An Information Geometry Approach to Out-of-Distribution Detection [Code] by Gomes et al.
(ICLR 2022) How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? [Code] by Ming et al.
(ICLR 2022) VOS: Learning What You Don't Know by Virtual Outlier Synthesis [Code] by Du et al.
(AAAI 2022) On the Impact of Spurious Correlation for Out-of-distribution Detection [Code] by Ming et al.
(AAAI 2022) iDECODe: In-Distribution Equivariance for Conformal Out-of-Distribution Detection by Kaur et al.
(AAAI 2022) Provable Guarantees for Understanding Out-of-distribution Detection [Code] by Morteza and Li
(AAAI 2022) Learning Modular Structures That Generalize Out-of-Distribution (Student Abstract) by Ashok et al.
(AAAI 2022) Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes by Sun and Wang
(CVPR 2021) Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces [Code] by Zaeemzadeh et al.
(CVPR 2021) MOOD: Multi-level Out-of-distribution Detection [Code] by Lin et al.
(CVPR 2021) MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space [Code] by Huang and Li
(NeurIPS 2021) Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection [Code] by Bibas et al.
(NeurIPS 2021) STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data by Zhou et al.
(NeurIPS 2021) Exploring the Limits of Out-of-Distribution Detection [Code] by Fort et al.
(NeurIPS 2021) Learning Causal Semantic Representation for Out-of-Distribution Prediction [Code] by Liu et al.
(NeurIPS 2021) Towards optimally abstaining from prediction with OOD test examples by Kalai and Kanade
(NeurIPS 2021) Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models [Code] by Kim et al.
(NeurIPS 2021) RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection [Code] by Song et al.
(NeurIPS 2021) ⭐⭐⭐⭐⭐ ReAct: Out-of-distribution Detection With Rectified Activations [Code] by Sun et al.
(NeurIPS 2021) ⭐⭐⭐⭐⭐ (GradNorm) On the Importance of Gradients for Detecting Distributional Shifts in the Wild [Code] by Huang et al.
(NeurIPS 2021) Watermarking for Out-of-distribution Detection by Wang et al.
(NeurIPS 2021) Can multi-label classification networks know what they don't know? [Code] by Wang et al.
(ICLR 2021) SSD: A Unified Framework for Self-Supervised Outlier Detection [Code] by Sehwag et al.
(ICLR 2021) Multiscale Score Matching for Out-of-Distribution Detection [Code] by Mahmood et al.
(ICML 2021) Understanding Failures in Out-of-Distribution Detection with Deep Generative Models by Zhang et al.
(ICCV 2021) Semantically Coherent Out-of-Distribution Detection [Project Page] [Code] by Yang et al.
(ICCV 2021) CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue by Tang et al.
(ECCV 2021) DICE: Leveraging Sparsification for Out-of-Distribution Detection [Code] by Sun and Li
(CVPR 2020) Deep Residual Flow for Out of Distribution Detection [https://github.com/EvZissel/Residual-Flow] by Zisselman and Tamar
(CVPR 2020) [Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data] [Code] (https://arxiv.org/pdf/2002.11297.pdf) by Hsu et al.
(NeurIPS 2020) CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances [Code] by Tack et al.
(NeurIPS 2020) ⭐⭐⭐⭐⭐ Energy-based Out-of-distribution Detection [Code] by Liu et al.
(NeurIPS 2020) OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification [Video] by Jeong and Kim
(NeurIPS 2020) Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples [Code] by Nandy et al.
(NeurIPS 2020) Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder [Code] by Xiao et al.
(NeurIPS 2020) ⭐⭐⭐⭐⭐ Why Normalizing Flows Fail to Detect Out-of-Distribution Data [Code] by Kirichenko et al.
(ICML 2020) Detecting Out-of-Distribution Examples with Gram Matrices [Code] by Sastry and Oore
(CVPR 2019) Out-Of-Distribution Detection for Generalized Zero-Shot Action Recognition [Code] by Mandal et al.
(NeurIPS 2019) Likelihood Ratios for Out-of-Distribution Detection [Video] by Ren et al.
(ICCV 2019) Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy [Code] by Yu and Aizawa
(NeurIPS 2018) ⭐⭐⭐⭐⭐ (Mahalanobis) A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks [Code] by Lee et al.
(NeurIPS 2018) Out-of-Distribution Detection using Multiple Semantic Label Representations by Shalev et al.
(NeurIPS 2018) Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem [Code] by Hein et al.
(ICLR 2018) Do Deep Generative Models Know What They Don't Know? [Slides] by Nalisnick et al.
(ICLR 2018) ⭐⭐⭐⭐⭐ (OE) Deep Anomaly Detection with Outlier Exposure [Code] by Hendrycks et al.
(ICLR 2018) ⭐⭐⭐⭐⭐ (ODIN) Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks [Code] by Liang et al.
(ICLR 2018) Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples [Code] by Lee et al.
(ECCV 2018) Out-of-Distribution Detection Using an Ensemble of Self-Supervised Leave-out Classifiers [Code] by Vyas et al.
(ArXiv 2018) Learning Confidence for Out-of-Distribution Detection in Neural Networks [Code] by DeVries and Taylor
(ICLR 2017) ⭐⭐⭐⭐⭐ A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks [Code] by Hendrycks and Gimpel
(ICLR 2023) Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement by Lee et al.
(ICML 2023) Learning Unforeseen Robustness from Out-of-distribution Data Using Equivariant Domain Translator by Zhu et al.
(ICML 2023) Out-of-Domain Robustness via Targeted Augmentations [Code] by Gao et al.
(TMLR 2022) The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning by Andreassen et al.
(NeurIPS 2022) Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness by Pinto et al.
(NeurIPS 2022) Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free [Code] by Meinke et al.
(ICML 2022) Improving Out-of-Distribution Robustness via Selective Augmentation [Video] [Code] by Yao et al.
(NeurIPS 2021) A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness by Diffenderfer et al.
(ICLR 2021) In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness [Code] by Xie et al.
(NeurIPS 2020) Certifiably Adversarially Robust Detection of Out-of-Distribution Data [Code] by Bitterwolf et al.
(ICLR 2023) Improving Out-of-distribution Generalization with Indirection Representations by Pham et al.
(ICLR 2023) Topology-aware Robust Optimization for Out-of-Distribution Generalization [Code] by Qiao and Peng
(ICLR 2023) ⭐⭐⭐⭐⭐Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization by Kaur et al.
(ICML 2023) Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection [Video] by Bai et al.
(AAAI 2023) On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization by Xin et al.
(AAAI 2023) Certifiable Out-of-Distribution Generalization by Ye et al.
(AAAI 2023) Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization by Zhu et al.
(AAAI 2023) Out-of-Distribution Generalization by Neural-Symbolic Joint Training by Liu et al.
(CVPR 2022) Out-of-Distribution Generalization With Causal Invariant Transformations by Wang et al.
(CVPR 2022) OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization [Video] [Code] by Ye et al.
(NeurIPS 2022) Learning Invariant Graph Representations for Out-of-Distribution Generalization by Li et al.
(NeurIPS 2022) Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors by Wang et al.
(NeurIPS 2022) Functional Indirection Neural Estimator for Better Out-of-distribution Generalization by Pham et al.
(NeurIPS 2022) Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization [Code] by Zhang et al.
(NeurIPS 2022) Assaying Out-Of-Distribution Generalization in Transfer Learning [Code] by Wenzel et al.
(NeurIPS 2022) Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs [Code] by Chen et al.
(NeurIPS 2022) Diverse Weight Averaging for Out-of-Distribution Generalization [Code] by Ramé et al.
(NeurIPS 2022) ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization by Dong et al.
(ICML 2022) Certifying Out-of-Domain Generalization for Blackbox Functions [Code] by Weber et al.
(NeurIPS 2022) LOG: Active Model Adaptation for Label-Efficient OOD Generalization by Shao et al.
(ICML 2022) Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization [Code] by Ramé et al.
(ICLR 2022) Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations [Code] by Puli et al.
(ICLR 2022) Uncertainty Modeling for Out-of-Distribution Generalization [Code] by Li et al.
(ICLR 2022) Invariant Causal Representation Learning for Out-of-Distribution Generalization by Lu et al.
(AAAI 2022) VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization by Chen et al.
(CVPR 2021) Deep Stable Learning for Out-of-Distribution Generalization by Zhang et al.
(NeurIPS 2021) Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization [Video] by Ahuja et al.
(NeurIPS 2021) On the Out-of-distribution Generalization of Probabilistic Image Modelling by Zhang et al.
(NeurIPS 2021) On Calibration and Out-of-Domain Generalization [Video] by Wald et al.
(NeurIPS 2021) Towards a Theoretical Framework of Out-of-Distribution Generalization [Slides] by Ye et al.
(NeurIPS 2021) Out-of-Distribution Generalization in Kernel Regression by Canatar et al.
(NeurIPS 2021) Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning [Code] by Millbich et al.
(ICLR 2021) Understanding the failure modes of out-of-distribution generalization [Video] by Nagarajan et al.
(ICML 2021) Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [Code] by Miller et al.
(ICML 2021) Out-of-Distribution Generalization via Risk Extrapolation (REx) by Krueger et al.
(ICML 2021) Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? [Slides] by Zhang et al.
(ICML 2021) Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization [Code] by Baranwal et al.
(ICLR 2023) Harnessing Out-Of-Distribution Examples via Augmenting Content and Style by Huang et al.
(ICLR 2023) Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization [Code] by Chen et al.
(ICLR 2023) On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail Learning by Bai et al.
(ICLR 2023) Out-of-distribution Representation Learning for Time Series Classification by Lu et al.
(ICML 2023) Exploring Chemical Space with Score-based Out-of-distribution Generation [Code] by Lee et al.
(ICML 2023) The Value of Out-of-Distribution Data by Silva et al.
(ICML 2023) CLIPood: Generalizing CLIP to Out-of-Distributions by Shu et al.
(NeurIPS 2022) GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech [Code] by Huang et al.
(NeurIPS 2022) Learning Substructure Invariance for Out-of-Distribution Molecular Representations [Code] by Yang et al.
(NeurIPS 2022) Evaluating Out-of-Distribution Performance on Document Image Classifiers by Larson et al.
(NeurIPS 2022) OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs by Zhou et al.
(ICLR 2022) Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution by Kumar et al.
(ICML 2022) Improved StyleGAN-v2 based Inversion for Out-of-Distribution Images by Subramanyam et al.
(NeurIPS 2021) The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations [Slides] by Hase et al.
(NeurIPS 2021) POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples [Code] by Le et al.
(NeurIPS 2021) Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data [Code] by Park et al.
(ICLR 2021) Removing Undesirable Feature Contributions Using Out-of-Distribution Data by Lee et al.