Reappraising Domain Generalization in Neural Networks

Understand

Given that Neural Networks generalize unreasonably well in the IID setting (with benign overfitting and betterment in performance with more parameters), OOD presents a consistent failure case to better the understanding of how they learn.

• This paper focuses on Domain Generalization (DG), which is perceived as the front face of OOD generalization.
• We find that the presence of multiple domains incentivizes domain agnostic learning and is the primary reason for generalization in Tradition DG.
• We show that the state-of-the-art results can be obtained by borrowing ideas from IID generalization and the DG tailored methods fail to add any performance gains.