  We present a novel algorithm for domain adaptation using optimal transport.
In domain adaptation, the goal is to adapt a classifier trained on the source
domain samples to the target domain. In our method, we use optimal transport to
map target samples to the domain named source fiction. This domain differs from
the source but is accurately classified by the source domain classifier. Our
main idea is to generate a source fiction by c-cyclically monotone
transformation over the target domain. If samples with the same labels in two
domains are c-cyclically monotone, the optimal transport map between these
domains preserves the class-wise structure, which is the main goal of domain
adaptation. To generate a source fiction domain, we propose an algorithm that
is based on our finding that adversarial attacks are a c-cyclically monotone
transformation of the dataset. We conduct experiments on Digits and Modern
Office-31 datasets and achieve improvement in performance for simple discrete
optimal transport solvers for all adaptation tasks.
