awesome-out-of-distribution-detection

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awesome-out-of-distribution-detection

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Paper of out of distribution detection and generalization

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# awesome-out-of-distribution-detection

## Paper

### 2023 
[CVPR2023] [Block Selection Method for Using Feature Norm in Out-of-Distribution Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Block_Selection_Method_for_Using_Feature_Norm_in_Out-of-Distribution_Detection_CVPR_2023_paper.pdf)

[CVPR2023] [Uncertainty-Aware Optimal Transport for Semantically Coherent Out-of-Distribution Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Uncertainty-Aware_Optimal_Transport_for_Semantically_Coherent_Out-of-Distribution_Detection_CVPR_2023_paper.pdf)

[CVPR2023] [GEN: Pushing the Limits of Softmax-Based Out-of-Distribution Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_GEN_Pushing_the_Limits_of_Softmax-Based_Out-of-Distribution_Detection_CVPR_2023_paper.pdf)

[CVPR2023] [Detection of out-of-distribution samples using binary neuron activation patterns
](https://openaccess.thecvf.com/content/CVPR2023/papers/Olber_Detection_of_Out-of-Distribution_Samples_Using_Binary_Neuron_Activation_Patterns_CVPR_2023_paper.pdf)

[CVPR2023] [Decoupling MaxLogit for Out-of-Distribution Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Decoupling_MaxLogit_for_Out-of-Distribution_Detection_CVPR_2023_paper.pdf)

[CVPR2023] [Balanced Energy Regularization Loss for Out-of-distribution Detection](https://openaccess.thecvf.com/content/CVPR2023/papers/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.pdf)

[CVPR2023] [Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Rethinking_Out-of-Distribution_OOD_Detection_Masked_Image_Modeling_Is_All_You_CVPR_2023_paper.pdf)

[CVPR2023] [LINe: Out-of-Distribution Detection by Leveraging Important Neurons](https://openaccess.thecvf.com/content/CVPR2023/papers/Ahn_LINe_Out-of-Distribution_Detection_by_Leveraging_Important_Neurons_CVPR_2023_paper.pdf)

[ACL2023] [Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection](https://arxiv.org/pdf/2305.13282.pdf)

[ICML2023] [Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection](https://arxiv.org/pdf/2305.16966.pdf)

[ICML2023] [Unsupervised Out-of-Distribution Detection with Diffusion Inpainting](https://openreview.net/pdf?id=HiX1ybkFMl)

[ICML2023] [Concept-based Explanations for Out-of-Distribution Detectors](https://openreview.net/pdf?id=a33IYBCFey)

[ICML2023] [In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation](https://openreview.net/attachment?id=ChniRIfpRR&name=pdf)

[ICML2023] [Detecting Out-of-distribution Data through In-distribution Class Prior](https://openreview.net/attachment?id=charggEv8v&name=pdf)

[ICML2023] [Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability](https://openreview.net/attachment?id=9himkcdirP&name=pdf)

[ICML2023] [Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection](https://openreview.net/attachment?id=3FydczZwkJ&name=pdf)

[ICLR2023] [Agree to Disagree: Diversity through Disagreement for Better Transferability](https://openreview.net/forum?id=K7CbYQbyYhY)

[ICLR2023] [Out-of-Distribution Detection and Selective Generation for Conditional Language Models](https://openreview.net/forum?id=kJUS5nD0vPB)

[ICLR2023] [A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet](https://openreview.net/forum?id=Iuubb9W6Jtk)

[ICLR2023] [Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection](https://openreview.net/forum?id=mMNimwRb7Gr)

[ICLR2023] [Packed Ensembles for efficient uncertainty estimation](https://openreview.net/forum?id=XXTyv1zD9zD)

[ICLR2023] [Harnessing Out-Of-Distribution Examples via Augmenting Content and Style](https://openreview.net/forum?id=boNyg20-JDm)

[ICLR2023] [The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection](https://openreview.net/forum?id=YlGsTZODyjz)

[ICLR2023] [Energy-based Out-of-Distribution Detection for Graph Neural Networks](https://openreview.net/forum?id=zoz7Ze4STUL)

[ICLR2023] [Out-of-distribution Detection with Implicit Outlier Transformation](https://openreview.net/forum?id=hdghx6wbGuD)

[ICLR2023] [How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection?](https://openreview.net/forum?id=aEFaE0W5pAd) 🌟

[ICLR2023] [Efficient Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy](https://openreview.net/forum?id=KkazG4lgKL)

[ICLR2023] [Non-parametric Outlier Synthesis](https://openreview.net/forum?id=JHklpEZqduQ)

[ICLR2023] [Extremely Simple Activation Shaping for Out-of-Distribution Detection](https://openreview.net/forum?id=ndYXTEL6cZz)

[ICLR2023] [Out-of-distribution Representation Learning for Time Series Classification](https://openreview.net/forum?id=ndYXTEL6cZz)

### 2022 
[ICLR2022] [Uncertainty Modeling for Out-of-Distribution Generalization](https://openreview.net/pdf?id=6HN7LHyzGgC)

[ICLR2022] [Igeood: An Information Geometry Approach to Out-of-Distribution Detection](https://openreview.net/pdf?id=mfwdY3U_9ea)

[ICLR2022] [Revisiting flow generative models for Out-of-distribution detection](https://openreview.net/pdf?id=6y2KBh-0Fd9)

[ICLR2022] [A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks](https://openreview.net/pdf?id=Oy9WeuZD51)

[ICLR2022] [VOS: Learning What You Don't Know by Virtual Outlier Synthesis](https://openreview.net/pdf?id=TW7d65uYu5M)

[ICLR2022] [Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty](https://openreview.net/pdf?id=GQd7mXSPua)

[ICML2022] [Out-of-distribution detection with deep nearest neighbors](https://proceedings.mlr.press/v162/sun22d/sun22d.pdf)

[ICML2022] [Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition](https://proceedings.mlr.press/v162/wang22aq/wang22aq.pdf)[[code](https://github.com/amazon-science/long-tailed-ood-detection)]

[ICML2022] [Training OOD Detectors in their Natural Habitats](https://proceedings.mlr.press/v162/katz-samuels22a/katz-samuels22a.pdf)

[ICML2022] [Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities](https://proceedings.mlr.press/v162/bitterwolf22a/bitterwolf22a.pdf)

[ICML2022] [Scaling Out-of-Distribution Detection for Real-World Settings](https://proceedings.mlr.press/v162/hendrycks22a/hendrycks22a.pdf)

[ICML2022] [POEM: Out-of-Distribution Detection with Posterior Sampling](https://proceedings.mlr.press/v162/ming22a/ming22a.pdf)

[NeurIPS2022] [Deep Ensembles Work, But Are They Necessary?](https://arxiv.org/pdf/2202.06985.pdf)

[NeurIPS2022] [Watermarking for Out-of-distribution Detection](https://openreview.net/pdf?id=6rhl2k1SUGs)

[NeurIPS2022] [GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs](https://openreview.net/pdf?id=mSiPuHIP7t8)

[NeurIPS2022] [Out-of-Distribution Detection via Conditional Kernel Independence Model](https://openreview.net/pdf?id=rTTh1RIn6E)

[NeurIPS2022] [Beyond Mahalanobis Distance for Textual OOD Detection](https://openreview.net/pdf?id=ReB7CCByD6U)

[NeurIPS2022] [Boosting Out-of-distribution Detection with Typical Features](https://openreview.net/pdf?id=4maAiUt0A4)

[NeurIPS2022] [Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE](https://openreview.net/pdf?id=vMQ1V_z0TxU)

[NeurIPS2022] [RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection](https://openreview.net/pdf?id=-deKNiSOXLG)

[NeurIPS2022] [Your Out-of-Distribution Detection Method is Not Robust!](https://openreview.net/pdf?id=YUEP3ZmkL1)

[NeurIPS2022] [Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free](https://openreview.net/pdf?id=9ZWgrozGP0)

[NeurIPS2022] [Is Out-of-Distribution Detection Learnable?](https://openreview.net/pdf?id=sde_7ZzGXOE)

[NeurIPS2022] [SIREN: Shaping Representations for Detecting Out-of-Distribution Objects](https://openreview.net/pdf?id=8E8tgnYlmN)

[NeurIPS2022] [Delving into Out-of-Distribution Detection with Vision-Language Representations](https://openreview.net/pdf?id=KnCS9390Va)

[NeurIPS2022] [UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs](https://openreview.net/pdf?id=djOANbV2zSu)

[NeurIPS2022] [Density-driven Regularization for Out-of-distribution Detection](https://openreview.net/pdf?id=aZQJMVx8fk)

[ECCV2022] [Tailoring Self-Supervision for Supervised Learning](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136850342.pdf)[[Code](https://github.com/wjun0830/Localizable-Rotation)]

[NeurIPS2022 Workshop] [Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification](https://arxiv.org/pdf/2209.04493.pdf)

[NeurIPS2022 Workshop] [Out-of-Distribution Detection and Selective Generation for Conditional Language Models](https://arxiv.org/pdf/2209.15558.pdf)

### 2021
[ICLR2021] [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://openreview.net/pdf?id=v5gjXpmR8J)[[code](https://github.com/inspire-group/SSD)]

[ICLR2021] [Protecting DNNs from Theft using an Ensemble of Diverse Models](https://openreview.net/pdf?id=LucJxySuJcE)

[NeurIPS2021] [Exploring the Limits of Out-of-Distribution Detection](https://proceedings.neurips.cc/paper/2021/file/3941c4358616274ac2436eacf67fae05-Paper.pdf)

[NeurIPS2021] [On the Importance of Gradients for Detecting Distributional Shifts in the Wild](https://proceedings.neurips.cc/paper/2021/file/063e26c670d07bb7c4d30e6fc69fe056-Paper.pdf)[[code](https://github.com/deeplearning-wisc/gradnorm_ood)]

[NeurIPS2021] [Neural Ensemble Search for Uncertainty Estimation and Dataset Shift](https://proceedings.neurips.cc/paper/2021/file/41a6fd31aa2e75c3c6d427db3d17ea80-Paper.pdf)[[code](https://github.com/automl/nes)]

[NeurIPS2021] [ReAct: Out-of-distribution Detection With Rectified Activations](https://proceedings.neurips.cc/paper_files/paper/2021/file/01894d6f048493d2cacde3c579c315a3-Paper.pdf)

[NeurIPS2021] [Can multi-label classification networks know what they don’t know?](https://proceedings.neurips.cc/paper/2021/file/f3b7e5d3eb074cde5b76e26bc0fb5776-Paper.pdf)

[ICML2021] [Out-of-Distribution Generalization via Risk Extrapolation](http://proceedings.mlr.press/v139/krueger21a/krueger21a.pdf)

[ICML2021] [Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
](http://proceedings.mlr.press/v139/miller21b/miller21b.pdf)

[EMNLP2021] [kFolden: k-Fold Ensemble for Out-Of-Distribution Detection](https://arxiv.org/pdf/2108.12731.pdf)

[TVCG] [OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8994105)

### 2020
[ICLR2020] [Ensemble Distribution Distillation](https://openreview.net/pdf?id=BygSP6Vtvr)

[NeurIPS2020] [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://proceedings.neurips.cc/paper/2020/file/d8330f857a17c53d217014ee776bfd50-Paper.pdf)[[code](https://modestyachts.github.io/imagenet-testbed/)]

[NeurIPS2020] [Csi: Novelty detection via contrastive learning on distributionally shifted instances](https://proceedings.neurips.cc/paper/2020/file/8965f76632d7672e7d3cf29c87ecaa0c-Paper.pdf) [[code](https://github.com/alinlab/CSI)]

[NeurIPS2020] [Energy-based Out-of-distribution Detection](https://proceedings.neurips.cc/paper/2020/file/f5496252609c43eb8a3d147ab9b9c006-Paper.pdf)[[code](https://github.com/wetliu/energy_ood)]

[CVPR2020] [Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hsu_Generalized_ODIN_Detecting_Out-of-Distribution_Image_Without_Learning_From_Out-of-Distribution_Data_CVPR_2020_paper.pdf)

[CVPR2020] [Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w20/Gustafsson_Evaluating_Scalable_Bayesian_Deep_Learning_Methods_for_Robust_Computer_Vision_CVPRW_2020_paper.pdf)

### 2019
[ICLR2019] [Deep Anomaly Detection with Outlier Exposure](https://openreview.net/pdf?id=HyxCxhRcY7)

[NeurIPS2019] [Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift.](https://proceedings.neurips.cc/paper/2019/file/8558cb408c1d76621371888657d2eb1d-Paper.pdf)

[NeurIPS2019] [Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty](https://proceedings.neurips.cc/paper/2019/file/a2b15837edac15df90721968986f7f8e-Paper.pdf)

[NeurIPS2019] [Likelihood Ratios for Out-of-Distribution Detection](https://proceedings.neurips.cc/paper/2019/file/1e79596878b2320cac26dd792a6c51c9-Paper.pdf)

### 2018
[arxiv] [WAIC, but Why? Generative Ensembles for Robust Anomaly Detection](https://arxiv.org/abs/1810.01392):fire:

[ICLR2018] [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://arxiv.org/pdf/1711.09325.pdf) [[code](https://github.com/alinlab/Confident_classifier)]

[ICLR2018] [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://arxiv.org/pdf/1706.02690.pdf) [[code](https://github.com/facebookresearch/odin)] :fire:

[ECCV2018] [Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers](https://openaccess.thecvf.com/content_ECCV_2018/papers/Apoorv_Vyas_Out-of-Distribution_Detection_Using_ECCV_2018_paper.pdf)

[BMVC2018] [Metric Learning for Novelty and Anomaly Detection](https://arxiv.org/pdf/1808.05492.pdf)

[NeurIPS2018] [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://proceedings.neurips.cc/paper/2018/file/abdeb6f575ac5c6676b747bca8d09cc2-Paper.pdf)

### 2017
[ICLR2017] [A baseline for detecting misclassified and out-of-distribution examples in neural networks](https://arxiv.org/pdf/1610.02136.pdf) [[code](https://github.com/hendrycks/error-detection)]

[NeurIPS2017] [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://proceedings.neurips.cc/paper/2017/file/9ef2ed4b7fd2c810847ffa5fa85bce38-Paper.pdf):fire:

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