Python OOD-on-NAE Resources

🔥 DrugOOD 🔥 : OOD Dataset Curator and Benchmark for AI Aided Drug Discovery This is the official implementation of the DrugOOD project, this is the

OOD-on-NAE Research project on out of distribution detection for the Computer Vision course by Prof. Rob Fergus (CSCI-GA 2271) Paper out on arXiv - ht

Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization Code for reproducing our results in the Head2Toe paper. Paper: arxiv.or

Pretrained Transformers Improve Out-of-Distribution Robustness How does pretraining affect out-of-distribution robustness? We create an OOD benchmark

OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary Out-of-Distribution Minimum Anomaly Score GAN (OMASGAN) C

Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec

Generalized Out-of-Distribution Detection: A Survey 1. Overview This repository is with our survey paper: Title: Generalized Out-of-Distribution Detec

OOD Detection Load conda environment conda env create -f environment.yml or install requirements: while read requirement; do conda install --yes $requ

Uncertainty-based OOD detection Exploring the link between uncertainty estimates obtained by "exact" Bayesian inference and out-of-distribution (OOD)

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and

DEAR (Deep Evidential Action Recognition) Project | Paper & Supp Wentao Bao, Qi Yu, Yu Kong International Conference on Computer Vision (ICCV Oral), 2

Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

ood-text-emnlp Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them" Files fine_tune.py is used to finetune the GPT-2 mo

Contra-OOD Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers. Requirements PyTorch Transformers datasets

SCOOD-UDG (ICCV 2021) This repository is the official implementation of the paper: Semantically Coherent Out-of-Distribution Detection Jingkang Yang,

FOOD - Fast OOD Detector Pytorch implamentation of the confernce peper FOOD arxiv link. Abstract Deep neural networks (DNNs) perform well at classifyi

Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T