  The common assumption that train and test sets follow similar distributions
is often violated in deployment settings. Given multiple source domains, domain
generalization aims to create robust models capable of generalizing to new
unseen domains. To this end, most of existing studies focus on extracting
domain invariant features across the available source domains in order to
mitigate the effects of inter-domain distributional changes. However, this
approach may limit the model's generalization capacity by relying solely on
finding common features among the source domains. It overlooks the potential
presence of domain-specific characteristics that could be prevalent in a subset
of domains, potentially containing valuable information. In this work, a novel
architecture named Additive Disentanglement of Domain Features with Remix Loss
(ADRMX) is presented, which addresses this limitation by incorporating domain
variant features together with the domain invariant ones using an original
additive disentanglement strategy. Moreover, a new data augmentation technique
is introduced to further support the generalization capacity of ADRMX, where
samples from different domains are mixed within the latent space. Through
extensive experiments conducted on DomainBed under fair conditions, ADRMX is
shown to achieve state-of-the-art performance. Code will be made available at
GitHub after the revision process.
