  In this paper, we propose a new approach called Deep LogCORAL for
unsupervised visual domain adaptation. Our work builds on the recently proposed
Deep CORAL method, which proposed to train a convolutional neural network and
simultaneously minimize the Euclidean distance of convariance matrices between
the source and target domains. We propose to use the Riemannian distance,
approximated by Log-Euclidean distance, to replace the naive Euclidean distance
in Deep CORAL. We also consider first-order information, and minimize the
distance of mean vectors between two domains. We build an end-to-end model, in
which we minimize both the classification loss, and the domain difference based
on the first and second order information between two domains. Our experiments
on the benchmark Office dataset demonstrate the improvements of our newly
proposed Deep LogCORAL approach over the Deep CORAL method, as well as further
improvement when optimizing both orders of information.
