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

A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization

RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions

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

👽 Out-of-Distribution Detection with PyTorch

Official PyTorch implementation and pretrained models of Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling Is All You Need (MOOD in short). Our paper is accepted by CVPR2023.

[ICCV 2021 Oral] Deep Evidential Action Recognition

Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)

Robust Out-of-distribution Detection in Neural Networks

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

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.

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).

The official implementation for Diffusion Denoising Process for Perceptron Bias in Out-of-distribution Detection (DiffOOD)

Paper of out of distribution detection and generalization

Code for Paper: Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data

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

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

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