  Domain adaptation aims to generalise a high-performance learner on target
domain (non-labelled data) by leveraging the knowledge from source domain (rich
labelled data) which comes from a different but related distribution. Assuming
the source and target domains data(e.g. images) come from a joint distribution
but follow on different marginal distributions, the domain adaptation work aims
to infer the joint distribution from the source and target domain to learn the
domain invariant features. Therefore, in this study, I extend the existing
state-of-the-art approach to solve the domain adaptation problem. In
particular, I propose a new approach to infer the joint distribution of images
from different distributions, namely Wasserstein Distance Guided Cross-Domain
Learning (WDGCDL). WDGCDL applies the Wasserstein distance to estimate the
divergence between the source and target distribution which provides good
gradient property and promising generalisation bound. Moreover, to tackle the
training difficulty of the proposed framework, I propose two different training
schemes for stable training. Qualitative results show that this new approach is
superior to the existing state-of-the-art methods in the standard domain
adaptation benchmark.
