[ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond

We leverage 14 datasets as OOD test data and conduct evaluations on 8 NLU tasks over 21 popularly used models. Our findings confirm that the OOD accuracy in NLP tasks needs to be paid more attention to since the significant performance decay compared to ID accuracy has been found in all settings.

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.

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

💠 Essential object oriented design (python, pytest, travisCI)

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

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.

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

Reproducing experimental results of OOD-by-MCD [Yu and Aizawa et al. ICCV 2019]

A Python wrapper for Affinity (CRM platform).

Repository for the paper "Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey"

Multilabel Out-of-Distribution Detection

KAGE-Bench: pure JAX 2D platformer RL benchmark for visual OOD generalization. Massively-parallel (vmap/JIT) RGB env with YAML-configurable visuals/physics, plus PPO-CNN (Flax) training scripts.

[ICML 2023] "Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability"

Implement the OOD milestone papers with pyotorch template style

A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures

Realisitic Out-of-Distribution (OOD) Detection

This is the code repository for the 3D Semantic Novelty Detection via Large-Scale Pre-Trained Models

A reconstructed mini-skeleton of LiquidLib written in Python