  Distributional shift between domains poses great challenges to modern machine
learning algorithms. The domain generalization (DG) signifies a popular line
targeting this issue, where these methods intend to uncover universal patterns
across disparate distributions. Noted, the crucial challenge behind DG is the
existence of irrelevant domain features, and most prior works overlook this
information. Motivated by this, we propose a novel contrastive-based
disentanglement method CDDG, to effectively utilize the disentangled features
to exploit the over-looked domain-specific features, and thus facilitating the
extraction of the desired cross-domain category features for DG tasks.
Specifically, CDDG learns to decouple inherent mutually exclusive features by
leveraging them in the latent space, thus making the learning discriminative.
Extensive experiments conducted on various benchmark datasets demonstrate the
superiority of our method compared to other state-of-the-art approaches.
Furthermore, visualization evaluations confirm the potential of our method in
achieving effective feature disentanglement.
