  A complex combination of simultaneous supervised-unsupervised learning is
believed to be the key to humans performing tasks seamlessly across multiple
domains or tasks. This phenomenon of cross-domain learning has been very well
studied in domain adaptation literature. Recent domain adaptation works rely on
an indirect way of first aligning the source and target domain distributions
and then train a classifier on the labeled source domain to classify the target
domain. However, this approach has the main drawback that obtaining a
near-perfect alignment of the domains in itself might be difficult/impossible
(e.g., language domains). To address this, we follow Vapnik's imperative of
statistical learning that states any desired problem should be solved in the
most direct way rather than solving a more general intermediate task and
propose a direct approach to domain adaptation that does not require domain
alignment. We propose a model referred Contradistinguisher that learns
contrastive features and whose objective is to jointly learn to
contradistinguish the unlabeled target domain in an unsupervised way and
classify in a supervised way on the source domain. We achieve the
state-of-the-art on Office-31 and VisDA-2017 datasets in both single-source and
multi-source settings. We also notice that the contradistinguish loss improves
the model performance by increasing the shape bias.
