  Transductive Adversarial Networks (TAN) is a novel domain-adaptation machine
learning framework that is designed for learning a conditional probability
distribution on unlabelled input data in a target domain, while also only
having access to: (1) easily obtained labelled data from a related source
domain, which may have a different conditional probability distribution than
the target domain, and (2) a marginalised prior distribution on the labels for
the target domain. TAN leverages a fully adversarial training procedure and a
unique generator/encoder architecture which approximates the transductive
combination of the available source- and target-domain data. A benefit of TAN
is that it allows the distance between the source- and target-domain
label-vector marginal probability distributions to be greater than 0 (i.e.
different tasks across the source and target domains) whereas other
domain-adaptation algorithms require this distance to equal 0 (i.e. a single
task across the source and target domains). TAN can, however, still handle the
latter case and is a more generalised approach to this case. Another benefit of
TAN is that due to being a fully adversarial algorithm, it has the potential to
accurately approximate highly complex distributions. Theoretical analysis
demonstrates the viability of the TAN framework.
