  Domain generalization faces challenges due to the distribution shift between
training and testing sets, and the presence of unseen target domains. Common
solutions include domain alignment, meta-learning, data augmentation, or
ensemble learning, all of which rely on domain labels or domain adversarial
techniques. In this paper, we propose a Dual-Stream Separation and
Reconstruction Network, dubbed DSDRNet. It is a disentanglement-reconstruction
approach that integrates features of both inter-instance and intra-instance
through dual-stream fusion. The method introduces novel supervised signals by
combining inter-instance semantic distance and intra-instance similarity.
Incorporating Adaptive Instance Normalization (AdaIN) into a two-stage cyclic
reconstruction process enhances self-disentangled reconstruction signals to
facilitate model convergence. Extensive experiments on four benchmark datasets
demonstrate that DSDRNet outperforms other popular methods in terms of domain
generalization capabilities.
