  Domain adaptation aims at training a classifier in one dataset and applying
it to a related but not identical dataset. One successfully used framework of
domain adaptation is to learn a transformation to match both the distribution
of the features (marginal distribution), and the distribution of the labels
given features (conditional distribution). In this paper, we propose a new
domain adaptation framework named Deep Transfer Network (DTN), where the highly
flexible deep neural networks are used to implement such a distribution
matching process.
  This is achieved by two types of layers in DTN: the shared feature extraction
layers which learn a shared feature subspace in which the marginal
distributions of the source and the target samples are drawn close, and the
discrimination layers which match conditional distributions by classifier
transduction. We also show that DTN has a computation complexity linear to the
number of training samples, making it suitable to large-scale problems. By
combining the best paradigms in both worlds (deep neural networks in
recognition, and matching marginal and conditional distributions in domain
adaptation), we demonstrate by extensive experiments that DTN improves
significantly over former methods in both execution time and classification
accuracy.
