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

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

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

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.

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

[ICCV 2021 Oral] Deep Evidential Action Recognition

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

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.

Robust Out-of-distribution Detection in Neural Networks

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

Paper of out of distribution detection and generalization

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

Code for the AAAI 2022 publication "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

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.

TensorFlow 2 implementation of the paper Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data (https://arxiv.org/abs/2002.11297).

[ICLR 2024 Spotlight] R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning