Traditional classifiers are deployed under closed-set setting, with both training and test
classes belong to the same set. However, real-world applications probably face the input of …

Abstract Domain generalization (DG) aims to learn a generalized model to an unseen target
domain using only limited source domains. Previous attempts to DG fail to learn domain …

Abstract Invariant Risk Minimization (IRM) is an emerging invariant feature extracting
technique to help generalization with distributional shift. However, we find that there exists a …

A fundamental challenge for machine learning models is generalizing to out-of-distribution
(OOD) data, in part due to spurious correlations. To tackle this challenge, we first formalize …

Despite the remarkable performance that modern deep neural networks have achieved on
independent and identically distributed (IID) data, they can crash under distribution shifts …

Generalization to out-of-distribution (OOD) data is one of the central problems in modern
machine learning. Recently, there is a surge of attempts to propose algorithms that mainly …

Deep learning has achieved tremendous success with independent and identically
distributed (iid) data. However, the performance of neural networks often degenerates …

Neural networks trained with SGD were recently shown to rely preferentially on linearly-
predictive features and can ignore complex, equally-predictive ones. This simplicity bias can …

Out-of-distribution (OOD) detection aims to detect “unknown” data whose labels have not
been seen during the in-distribution (ID) training process. Recent progress in representation …

Abstract Domain generalization (DG) aims to learn a robust model from source domains that
generalize well on unseen target domains. Recent studies focus on generating novel …