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

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

Python package to accelerate research on generalized out-of-distribution (OOD) detection.

👽 Out-of-Distribution Detection with PyTorch

"A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?" (CVPR 2024)

🔎 Multi-Class Hypersphere Anomaly Detection

✌[ICLR 2024] Class Incremental Learning via Likelihood Ratio Based Task Prediction

This is an official implementation for "Block Selection Method for Using Feature Norm in Out-of-distribution Detection".

An end-to-end vision and language model incorporating explicit knowledge graphs and OOD-detection.

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

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

The implementation of DS-FL+ (Energy-based Knowledge Distillation for Communication-Efficient Federated Learning, IEICE 2024 student poster session).

[ICLR 2024 Spotlight] "Negative Label Guided OOD Detection with Pretrained Vision-Language Models"

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

[WACV 2024] Official PyTorch implementation of Brainomaly

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

An end-to-end multimodal framework incorporating explicit knowledge graphs and OOD-detection. (NeurIPS23)

[ICCV 2023] The official implementation of paper "DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models"

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.

Python code to implement an efficient approach to unsupervised OOD detection with VAE