Domain Adversarial Tangent Learning Towards Interpretable Domain Adaptation.

Deep learning struggles to generalize well to an unseen target domain of interest.Current domain adaptation methods simultaneously learn a classifier and an adversarial game for invariant representations but inadequately align local structures, while the underlying process is hard to interpret.We propose a new interpretable adversarial domain architecture, matching local manifold approximations across domains.Evaluated against related networks, the approach is competitive, while the adaptation process can be visually verified.