  Multi-Source Domain Adaptation (MSDA) deals with the transfer of task
knowledge from multiple labeled source domains to an unlabeled target domain,
under a domain-shift. Existing methods aim to minimize this domain-shift using
auxiliary distribution alignment objectives. In this work, we present a
different perspective to MSDA wherein deep models are observed to implicitly
align the domains under label supervision. Thus, we aim to utilize implicit
alignment without additional training objectives to perform adaptation. To this
end, we use pseudo-labeled target samples and enforce a classifier agreement on
the pseudo-labels, a process called Self-supervised Implicit Alignment
(SImpAl). We find that SImpAl readily works even under category-shift among the
source domains. Further, we propose classifier agreement as a cue to determine
the training convergence, resulting in a simple training algorithm. We provide
a thorough evaluation of our approach on five benchmarks, along with detailed
insights into each component of our approach.
