  Domain generalization (DG) methods aim to achieve generalizability to an
unseen target domain by using only training data from the source domains.
Although a variety of DG methods have been proposed, a recent study shows that
under a fair evaluation protocol, called DomainBed, the simple empirical risk
minimization (ERM) approach works comparable to or even outperforms previous
methods. Unfortunately, simply solving ERM on a complex, non-convex loss
function can easily lead to sub-optimal generalizability by seeking sharp
minima. In this paper, we theoretically show that finding flat minima results
in a smaller domain generalization gap. We also propose a simple yet effective
method, named Stochastic Weight Averaging Densely (SWAD), to find flat minima.
SWAD finds flatter minima and suffers less from overfitting than does the
vanilla SWA by a dense and overfit-aware stochastic weight sampling strategy.
SWAD shows state-of-the-art performances on five DG benchmarks, namely PACS,
VLCS, OfficeHome, TerraIncognita, and DomainNet, with consistent and large
margins of +1.6% averagely on out-of-domain accuracy. We also compare SWAD with
conventional generalization methods, such as data augmentation and consistency
regularization methods, to verify that the remarkable performance improvements
are originated from by seeking flat minima, not from better in-domain
generalizability. Last but not least, SWAD is readily adaptable to existing DG
methods without modification; the combination of SWAD and an existing DG method
further improves DG performances. Source code is available at
https://github.com/khanrc/swad.
