Conditional Independence Induced Unsupervised Domain Adaptation

Learning domain-adaptive features is important to tackle the dataset bias problem, where data distributions in the labeled source domain and the unlabeled target domain can be different. The critical issue is to identify and then reduce the redundant information including class-irrelevant and domain-specific features. In this paper, a conditional independence induced unsupervised domain adaptation (CIDA) method is proposed to tackle the challenges. It aims to find the low-dimensional and transferable feature representation of each observation, namely the latent variable in the domain-adaptive subspace. Technically, two mutual information terms are optimized at the same time. One is the mutual information between the latent variable and the class label, and the other is the mutual information between the latent variable and the domain label. Note that the key module can be approximately reformulated as a conditional independence/dependence based optimization problem, and thus, it has a probabilistic interpretation with the Gaussian process. Temporary labels of the target samples and the model parameters are alternatively optimized. The objective function can be incorporated with deep network architectures, and the algorithm is implemented iteratively in an end-to-end manner. Extensive experiments are conducted on several benchmark datasets, and the results show effectiveness of CIDA. ©2023 Elsevier Ltd.