Neural Information Processing Systems · 2017 · 161 citations · 0 references
ComputingComputer ScienceMachine LearningNatural Language ProcessingStatistical LearningGenerative ModelingJoint DistributionsProbabilistic Machine LearningSemi-supervised LearningSupervised LearningUnsupervised LearningConditional EntropyDistribution MatchingJoint Distribution EstimationSemi-supervised Models
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.