Wasserstein Distance Guided Representation Learning for Domain Adaptation

Understand

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).