Out-of-distribution detection, robustness, and generalization resources. The repository contains a curated list of papers, tutorials, books, videos, articles and open-source libraries etc

The Official Repository for "Generalized OOD Detection: A Survey"

[NeurIPS 2023] RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions

👽 Out-of-Distribution Detection with PyTorch

[ECCV'22 Oral] Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes

[ICCV 2021 Oral] Deep Evidential Action Recognition

[ICCV'23] Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation

[SafeAI'21] Feature Space Singularity for Out-of-Distribution Detection.

We propose a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection to various types of adversarial OOD inputs and establishes state-of-the-art performance.

Robust Out-of-distribution Detection in Neural Networks

A project to add scalable state-of-the-art out-of-distribution detection (open set recognition) support by changing two lines of code! Perform efficient inferences (i.e., do not increase inference time) and detection without classification accuracy drop, hyperparameter tuning, or collecting additional data.

ICCV 2023: CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No

[ICCV'23 Oral] Unmasking Anomalies in Road-Scene Segmentation

The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.

Out-of-distribution detection using the pNML regret. NeurIPS2021

Official PyTorch implementation of MOOD series: (1) MOODv1: Rethinking Out-of-distributionDetection: Masked Image Modeling Is All You Need. (2) MOODv2: Masked Image Modeling for Out-of-Distribution Detection.

Code for "BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayeisan Fine-tuning"

Fair and explainable ML workshop

A project to improve out-of-distribution detection (open set recognition) and uncertainty estimation by changing a few lines of code in your project! Perform efficient inferences (i.e., do not increase inference time) without repetitive model training, hyperparameter tuning, or collecting additional data.

PyTorch Implementation of ECCV 2024 OOD-CV Workshop SSB Challenge (Open-Set Recognition Track) - 1st Place