Continuous Disentangled Joint Space Learning for Domain Generalization.

Domain generalization (DG) aims to learn a model on one or multiple observed source domains that can generalize to unseen target test domains. Previous approaches have focused on extracting domain-invariant information from multiple source domains, but domain-specific information is also closely tied to semantics in individual domains and is not well-suited for generalization to the target domain. In this article, we propose a novel DG method called continuous disentangled joint space learning (CJSL), which leverages both domain-invariant and domain-specific information for more effective DG. The key idea behind CJSL is to formulate and learn a continuous joint space (CJS) for domain-specific representations from source domains through iterative feature disentanglement. This learned CJS can then be used to simulate domain-specific representations for test samples from a mixture of multiple domains via Monte Carlo sampling during the inference stage. Unlike existing approaches, which exploit domain-invariant feature vectors only or aim to learn a universal domain-specific feature extractor, we simulate domain-specific representations via sampling the latent vectors in the learned CJS for the test sample to fully use the power of multiple domain-specific classifiers for robust prediction. Empirical results demonstrate that CJSL outperforms 19 state-of-the-art (SOTA) methods on seven benchmarks, indicating the effectiveness of our proposed method.