#Ood
Showing 4 of 4 repositories tagged #ood, ranked by stars
💎 The Principles of OOD (SOLID) based on Uncle Bob articles.
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.