  The primary objective of domain adaptation methods is to transfer knowledge
from a source domain to a target domain that has similar but different data
distributions. Thus, in order to correctly classify the unlabeled target domain
samples, the standard approach is to learn a common representation for both
source and target domain, thereby indirectly addressing the problem of learning
a classifier in the target domain. However, such an approach does not address
the task of classification in the target domain directly. In contrast, we
propose an approach that directly addresses the problem of learning a
classifier in the unlabeled target domain. In particular, we train a classifier
to correctly classify the training samples while simultaneously classifying the
samples in the target domain in an unsupervised manner. The corresponding model
is referred to as Discriminative Encoding for Domain Adaptation (DEDA). We show
that this simple approach for performing unsupervised domain adaptation is
indeed quite powerful. Our method achieves state of the art results in
unsupervised adaptation tasks on various image classification benchmarks. We
also obtained state of the art performance on domain adaptation in Amazon
reviews sentiment classification dataset. We perform additional experiments
when the source data has less labeled examples and also on zero-shot domain
adaptation task where no target domain samples are used for training.
