  Agnostic domain shift is the main reason of model degradation on the unknown
target domains, which brings an urgent need to develop Domain Generalization
(DG). Recent advances at DG use dynamic networks to achieve training-free
adaptation on the unknown target domains, termed Dynamic Domain Generalization
(DDG), which compensates for the lack of self-adaptability in static models
with fixed weights. The parameters of dynamic networks can be decoupled into a
static and a dynamic component, which are designed to learn domain-invariant
and domain-specific features, respectively. Based on the existing arts, in this
work, we try to push the limits of DDG by disentangling the static and dynamic
components more thoroughly from an optimization perspective. Our main
consideration is that we can enable the static component to learn
domain-invariant features more comprehensively by augmenting the
domain-specific information. As a result, the more comprehensive
domain-invariant features learned by the static component can then enforce the
dynamic component to focus more on learning adaptive domain-specific features.
To this end, we propose a simple yet effective Parameter Exchange (PE) method
to perturb the combination between the static and dynamic components. We
optimize the model using the gradients from both the perturbed and
non-perturbed feed-forward jointly to implicitly achieve the aforementioned
disentanglement. In this way, the two components can be optimized in a
mutually-beneficial manner, which can resist the agnostic domain shifts and
improve the self-adaptability on the unknown target domain. Extensive
experiments show that PE can be easily plugged into existing dynamic networks
to improve their generalization ability without bells and whistles.
