  Due to domain shift, deep neural networks (DNNs) usually fail to generalize
well on unknown test data in practice. Domain generalization (DG) aims to
overcome this issue by capturing domain-invariant representations from source
domains. Motivated by the insight that only partial parameters of DNNs are
optimized to extract domain-invariant representations, we expect a general
model that is capable of well perceiving and emphatically updating such
domain-invariant parameters. In this paper, we propose two modules of Domain
Decoupling and Combination (DDC) and Domain-invariance-guided Backpropagation
(DIGB), which can encourage such general model to focus on the parameters that
have a unified optimization direction between pairs of contrastive samples. Our
extensive experiments on two benchmarks have demonstrated that our proposed
method has achieved state-of-the-art performance with strong generalization
capability.
