  Recent works in domain adaptation always learn domain invariant features to
mitigate the gap between the source and target domains by adversarial methods.
The category information are not sufficiently used which causes the learned
domain invariant features are not enough discriminative. We propose a new
domain adaptation method based on prototype construction which likes capturing
data cluster centers. Specifically, it consists of two parts: disentanglement
and reconstruction. First, the domain specific features and domain invariant
features are disentangled from the original features. At the same time, the
domain prototypes and class prototypes of both domains are estimated. Then, a
reconstructor is trained by reconstructing the original features from the
disentangled domain invariant features and domain specific features. By this
reconstructor, we can construct prototypes for the original features using
class prototypes and domain prototypes correspondingly. In the end, the feature
extraction network is forced to extract features close to these prototypes. Our
contribution lies in the technical use of the reconstructor to obtain the
original feature prototypes which helps to learn compact and discriminant
features. As far as we know, this idea is proposed for the first time.
Experiment results on several public datasets confirm the state-of-the-art
performance of our method.
