  In the presence of large sets of labeled data, Deep Learning (DL) has
accomplished extraordinary triumphs in the avenue of computer vision,
particularly in object classification and recognition tasks. However, DL cannot
always perform well when the training and testing images come from different
distributions or in the presence of domain shift between training and testing
images. They also suffer in the absence of labeled input data. Domain
adaptation (DA) methods have been proposed to make up the poor performance due
to domain shift. In this paper, we present a new unsupervised deep domain
adaptation method based on the alignment of second order statistics
(covariances) as well as maximum mean discrepancy of the source and target data
with a two stream Convolutional Neural Network (CNN). We demonstrate the
ability of the proposed approach to achieve state-of the-art performance for
image classification on three benchmark domain adaptation datasets: Office-31
[27], Office-Home [37] and Office-Caltech [8].
