Conditional Independence Induced Unsupervised Domain Adaptation

•A novel method CIDA, which uses the independence/dependence between features and the corresponding class/domain labels, is proposed to deal with UDA problem.•The objective function can be embedded into deep network architectures, and the whole model can be trained in an end-to-end manner.•A probabilistic interpretation of CIDA with Gaussian process is presented.•The effectiveness of CIDA is validated by several UDA benchmark datasets.