  Unsupervised domain adaptation (UDA) conventionally assumes labeled source
samples coming from a single underlying source distribution. Whereas in
practical scenario, labeled data are typically collected from diverse sources.
The multiple sources are different not only from the target but also from each
other, thus, domain adaptater should not be modeled in the same way. Moreover,
those sources may not completely share their categories, which further brings a
new transfer challenge called category shift. In this paper, we propose a deep
cocktail network (DCTN) to battle the domain and category shifts among multiple
sources. Motivated by the theoretical results in \cite{mansour2009domain}, the
target distribution can be represented as the weighted combination of source
distributions, and, the multi-source unsupervised domain adaptation via DCTN is
then performed as two alternating steps: i) It deploys multi-way adversarial
learning to minimize the discrepancy between the target and each of the
multiple source domains, which also obtains the source-specific perplexity
scores to denote the possibilities that a target sample belongs to different
source domains. ii) The multi-source category classifiers are integrated with
the perplexity scores to classify target sample, and the pseudo-labeled target
samples together with source samples are utilized to update the multi-source
category classifier and the feature extractor. We evaluate DCTN in three domain
adaptation benchmarks, which clearly demonstrate the superiority of our
framework.
