  Domain adaptation is an important but challenging task. Most of the existing
domain adaptation methods struggle to extract the domain-invariant
representation on the feature space with entangling domain information and
semantic information. Different from previous efforts on the entangled feature
space, we aim to extract the domain invariant semantic information in the
latent disentangled semantic representation (DSR) of the data. In DSR, we
assume the data generation process is controlled by two independent sets of
variables, i.e., the semantic latent variables and the domain latent variables.
Under the above assumption, we employ a variational auto-encoder to reconstruct
the semantic latent variables and domain latent variables behind the data. We
further devise a dual adversarial network to disentangle these two sets of
reconstructed latent variables. The disentangled semantic latent variables are
finally adapted across the domains. Experimental studies testify that our model
yields state-of-the-art performance on several domain adaptation benchmark
datasets.
