  In image classification, it is often expensive and time-consuming to acquire
sufficient labels. To solve this problem, domain adaptation often provides an
attractive option given a large amount of labeled data from a similar nature
but different domain. Existing approaches mainly align the distributions of
representations extracted by a single structure and the representations may
only contain partial information, e.g., only contain part of the saturation,
brightness, and hue information. Along this line, we propose
Multi-Representation Adaptation which can dramatically improve the
classification accuracy for cross-domain image classification and specially
aims to align the distributions of multiple representations extracted by a
hybrid structure named Inception Adaptation Module (IAM). Based on this, we
present Multi-Representation Adaptation Network (MRAN) to accomplish the
cross-domain image classification task via multi-representation alignment which
can capture the information from different aspects. In addition, we extend
Maximum Mean Discrepancy (MMD) to compute the adaptation loss. Our approach can
be easily implemented by extending most feed-forward models with IAM, and the
network can be trained efficiently via back-propagation. Experiments conducted
on three benchmark image datasets demonstrate the effectiveness of MRAN. The
code has been available at https://github.com/easezyc/deep-transfer-learning.
