awesome-out-of-distribution-detection

Paper

2023

[ICLR2023] Agree to Disagree: Diversity through Disagreement for Better Transferability

[ICLR2023] Out-of-Distribution Detection and Selective Generation for Conditional Language Models

[ICLR2023] A framework for benchmarking Class-out-of-distribution detection and its application to ImageNet

[ICLR2023] Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection

[ICLR2023] Packed Ensembles for efficient uncertainty estimation

[ICLR2023] Harnessing Out-Of-Distribution Examples via Augmenting Content and Style

[ICLR2023] The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection

[ICLR2023] Energy-based Out-of-Distribution Detection for Graph Neural Networks

[ICLR2023] Out-of-distribution Detection with Implicit Outlier Transformation

[ICLR2023] How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? 🌟

[ICLR2023] Efficient Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy

[ICLR2023] Non-parametric Outlier Synthesis

[ICLR2023] Extremely Simple Activation Shaping for Out-of-Distribution Detection

[ICLR2023] Out-of-distribution Representation Learning for Time Series Classification

2022

[ICLR2022] Uncertainty Modeling for Out-of-Distribution Generalization

[ICLR2022] Igeood: An Information Geometry Approach to Out-of-Distribution Detection

[ICLR2022] Revisiting flow generative models for Out-of-distribution detection

[ICLR2022] A Statistical Framework for Efficient Out of Distribution Detection in Deep Neural Networks

[ICLR2022] VOS: Learning What You Don't Know by Virtual Outlier Synthesis

[ICLR2022] Meta Learning Low Rank Covariance Factors for Energy Based Deterministic Uncertainty

[ICML2022] Out-of-distribution detection with deep nearest neighbors

[ICML2022] Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition[code]

[ICML2022] Training OOD Detectors in their Natural Habitats

[ICML2022] Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities

[ICML2022] Scaling Out-of-Distribution Detection for Real-World Settings

[ICML2022] POEM: Out-of-Distribution Detection with Posterior Sampling

[NeurIPS2022] Deep Ensembles Work, But Are They Necessary?

[NeurIPS2022] Watermarking for Out-of-distribution Detection

[NeurIPS2022] GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs

[NeurIPS2022] Out-of-Distribution Detection via Conditional Kernel Independence Model

[NeurIPS2022] Beyond Mahalanobis Distance for Textual OOD Detection

[NeurIPS2022] Boosting Out-of-distribution Detection with Typical Features

[NeurIPS2022] Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE

[NeurIPS2022] RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection

[NeurIPS2022] Your Out-of-Distribution Detection Method is Not Robust!

[NeurIPS2022] Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free

[NeurIPS2022] Is Out-of-Distribution Detection Learnable?

[NeurIPS2022] SIREN: Shaping Representations for Detecting Out-of-Distribution Objects

[NeurIPS2022] Delving into Out-of-Distribution Detection with Vision-Language Representations

[NeurIPS2022] UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs

[NeurIPS2022] Density-driven Regularization for Out-of-distribution Detection

[ECCV2022] Tailoring Self-Supervision for Supervised Learning[Code]

[NeurIPS2022 Workshop] Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification

[NeurIPS2022 Workshop] Out-of-Distribution Detection and Selective Generation for Conditional Language Models

2021

[ICLR2021] SSD: A Unified Framework for Self-Supervised Outlier Detection[code]

[ICLR2021] Protecting DNNs from Theft using an Ensemble of Diverse Models

[NeurIPS2021] Exploring the Limits of Out-of-Distribution Detection

[NeurIPS2021] On the Importance of Gradients for Detecting Distributional Shifts in the Wild[code]

[NeurIPS2021] Neural Ensemble Search for Uncertainty Estimation and Dataset Shift[code]

[NeurIPS2021] Can multi-label classification networks know what they don’t know?

[ICML2021] Out-of-Distribution Generalization via Risk Extrapolation

[ICML2021] Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

[EMNLP2021] kFolden: k-Fold Ensemble for Out-Of-Distribution Detection

[TVCG] OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples

2020

[ICLR2020] Ensemble Distribution Distillation

[NeurIPS2020] Measuring Robustness to Natural Distribution Shifts in Image Classification[code]

[NeurIPS2020] Csi: Novelty detection via contrastive learning on distributionally shifted instances [code]

[NeurIPS2020] Energy-based Out-of-distribution Detection[code]

[CVPR2020] Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data

[CVPR2020] Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision

2019

[ICLR2019] Deep Anomaly Detection with Outlier Exposure

[NeurIPS2019] Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift.

[NeurIPS2019] Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

[NeurIPS2019] Likelihood Ratios for Out-of-Distribution Detection

2018

[arxiv] WAIC, but Why? Generative Ensembles for Robust Anomaly Detection🔥

[ICLR2018] Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples [code]

[ICLR2018] Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks [code] 🔥

[ECCV2018] Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers

[BMVC2018] Metric Learning for Novelty and Anomaly Detection

2017

[ICLR2017] A baseline for detecting misclassified and out-of-distribution examples in neural networks [code]

[NeurIPS2017] Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles🔥