  Deep learning methods can struggle to handle domain shifts not seen in
training data, which can cause them to not generalize well to unseen domains.
This has led to research attention on domain generalization (DG), which aims to
the model's generalization ability to out-of-distribution. Adversarial domain
generalization is a popular approach to DG, but conventional approaches (1)
struggle to sufficiently align features so that local neighborhoods are mixed
across domains; and (2) can suffer from feature space over collapse which can
threaten generalization performance. To address these limitations, we propose
localized adversarial domain generalization with space compactness
maintenance~(LADG) which constitutes two major contributions. First, we propose
an adversarial localized classifier as the domain discriminator, along with a
principled primary branch. This constructs a min-max game whereby the aim of
the featurizer is to produce locally mixed domains. Second, we propose to use a
coding-rate loss to alleviate feature space over collapse. We conduct
comprehensive experiments on the Wilds DG benchmark to validate our approach,
where LADG outperforms leading competitors on most datasets.
