  In this paper, we propose a simple model referred as Contradistinguisher
(CTDR) for unsupervised domain adaptation whose objective is to jointly learn
to contradistinguish on unlabeled target domain in a fully unsupervised manner
along with prior knowledge acquired by supervised learning on an entirely
different domain. Most recent works in domain adaptation rely on an indirect
way of first aligning the source and target domain distributions and then learn
a classifier on a labeled source domain to classify target domain. This
approach of an indirect way of addressing the real task of unlabeled target
domain classification has three main drawbacks. (i) The sub-task of obtaining a
perfect alignment of the domain in itself might be impossible due to large
domain shift (e.g., language domains). (ii) The use of multiple classifiers to
align the distributions unnecessarily increases the complexity of the neural
networks leading to over-fitting in many cases. (iii) Due to distribution
alignment, the domain-specific information is lost as the domains get morphed.
In this work, we propose a simple and direct approach that does not require
domain alignment. We jointly learn CTDR on both source and target distribution
for unsupervised domain adaptation task using contradistinguish loss for the
unlabeled target domain in conjunction with a supervised loss for labeled
source domain. Our experiments show that avoiding domain alignment by directly
addressing the task of unlabeled target domain classification using CTDR
achieves state-of-the-art results on eight visual and four language benchmark
domain adaptation datasets.
