CDOT: Continuous Domain Adaptation using Optimal Transport


In this work, we propose to exploit the geometry-awareness of optimal transport theory for the resolution of continuous domain adaptation problems. In particular, we address the scenario in which the target domain is continually, albeit slowly, evolving, and in which, at different time frames, we are given a batch of test data to classify. We propose a new regularized optimal transport model that takes into account the transportation cost, the entropy of the probabilistic coupling, the labels of the source domain, and the similarity between successive target domains. The resulting optimization problem is efficiently solved with a forward-backward splitting algorithm based on Bregman distances. Experiments show that the proposed approach leads to a significant improvement in terms of speed and performance with respect to the state of the art

Optimal Transport & Machine Learning Workhop (NeurIPS 2019)