  In this paper, we present a conditional GAN with two generators and a common
discriminator for multiview learning problems where observations have two
views, but one of them may be missing for some of the training samples. This is
for example the case for multilingual collections where documents are not
available in all languages. Some studies tackled this problem by assuming the
existence of view generation functions to approximately complete the missing
views; for example Machine Translation to translate documents into the missing
languages. These functions generally require an external resource to be set and
their quality has a direct impact on the performance of the learned multiview
classifier over the completed training set. Our proposed approach addresses
this problem by jointly learning the missing views and the multiview classifier
using a tripartite game with two generators and a discriminator. Each of the
generators is associated to one of the views and tries to fool the
discriminator by generating the other missing view conditionally on the
corresponding observed view. The discriminator then tries to identify if for an
observation, one of its views is completed by one of the generators or if both
views are completed along with its class. Our results on a subset of Reuters
RCV1/RCV2 collections show that the discriminator achieves significant
classification performance; and that the generators learn the missing views
with high quality without the need of any consequent external resource.
