In this work, we face the problem of unsupervised domain adaptation with a novel deep learning approach which leverages our finding that entropy minimization is induced by the optimal alignment of second order statistics between source and target domains. We formally demonstrate this hypothesis and, aiming at achieving an optimal alignment in practical cases, we adopt a more principled strategy which, differently from the current Euclidean approaches, deploys alignment along geodesics. Our pipeline can be implemented by adding to the standard classification loss (on the labeled source domain), a source-to-target regularizer that is weighted in an unsupervised and data-driven fashion. We provide extensive experiments to assess the superiority of our framework on standard domain and modality adaptation benchmarks.
A new unsupervised deep domain adaptation technique which efficiently unifies correlation alignment and entropy minimization Improves the correlation alignment approach to domain adaptation by replacing the Euclidean distance with the geodesic Log-Euclidean distance between two covariance matices, and automatically selecting the balancing cost by the entropy on the target domain. Proposal for minimal-entropy correlation alignment, an unsupervised domain adaptation algorithm which links together entropy minimization and correlation alignment methods.