  We investigate the non-identifiability issues associated with bidirectional
adversarial training for joint distribution matching. Within a framework of
conditional entropy, we propose both adversarial and non-adversarial approaches
to learn desirable matched joint distributions for unsupervised and supervised
tasks. We unify a broad family of adversarial models as joint distribution
matching problems. Our approach stabilizes learning of unsupervised
bidirectional adversarial learning methods. Further, we introduce an extension
for semi-supervised learning tasks. Theoretical results are validated in
synthetic data and real-world applications.
