  Recent works showed that Generative Adversarial Networks (GANs) can be
successfully applied in unsupervised domain adaptation, where, given a labeled
source dataset and an unlabeled target dataset, the goal is to train powerful
classifiers for the target samples. In particular, it was shown that a GAN
objective function can be used to learn target features indistinguishable from
the source ones. In this work, we extend this framework by (i) forcing the
learned feature extractor to be domain-invariant, and (ii) training it through
data augmentation in the feature space, namely performing feature augmentation.
While data augmentation in the image space is a well established technique in
deep learning, feature augmentation has not yet received the same level of
attention. We accomplish it by means of a feature generator trained by playing
the GAN minimax game against source features. Results show that both enforcing
domain-invariance and performing feature augmentation lead to superior or
comparable performance to state-of-the-art results in several unsupervised
domain adaptation benchmarks.

Computer Vision and Pattern Recognition