  Domain adaptation aims at generalizing a high-performance learner on a target
domain via utilizing the knowledge distilled from a source domain which has a
different but related data distribution. One solution to domain adaptation is
to learn domain invariant feature representations while the learned
representations should also be discriminative in prediction. To learn such
representations, domain adaptation frameworks usually include a domain
invariant representation learning approach to measure and reduce the domain
discrepancy, as well as a discriminator for classification. Inspired by
Wasserstein GAN, in this paper we propose a novel approach to learn domain
invariant feature representations, namely Wasserstein Distance Guided
Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by
the domain critic, to estimate empirical Wasserstein distance between the
source and target samples and optimizes the feature extractor network to
minimize the estimated Wasserstein distance in an adversarial manner. The
theoretical advantages of Wasserstein distance for domain adaptation lie in its
gradient property and promising generalization bound. Empirical studies on
common sentiment and image classification adaptation datasets demonstrate that
our proposed WDGRL outperforms the state-of-the-art domain invariant
representation learning approaches.

Machine Learning;Machine Learning