  Domain adaptation aims to mitigate the domain gap when transferring knowledge
from an existing labeled domain to a new domain. However, existing
disentanglement-based methods do not fully consider separation between
domain-invariant and domain-specific features, which means the domain-invariant
features are not discriminative. The reconstructed features are also not
sufficiently used during training. In this paper, we propose a novel enhanced
separable disentanglement (ESD) model. We first employ a disentangler to
distill domain-invariant and domain-specific features. Then, we apply feature
separation enhancement processes to minimize contamination between
domain-invariant and domain-specific features. Finally, our model reconstructs
complete feature vectors, which are used for further disentanglement during the
training phase. Extensive experiments from three benchmark datasets outperform
state-of-the-art methods, especially on challenging cross-domain tasks.
