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This paper proposes a semi-conditional normalizing flow model forsemi-supervised learning. The model uses both labelled and unlabeled data tolearn an explicit model of joint distribution over objects and labels.Semi-conditional architecture of the model allows us to efficiently compute avalue and gradients of the marginal likelihood for unlabeled objects. Theconditional part of the model is based on a proposed conditional couplinglayer. We demonstrate performance of the model for semi-supervisedclassification problem on different datasets. The model outperforms thebaseline approach based on variational auto-encoders on MNIST dataset.

Semi-Conditional Normalizing Flows for Semi-Supervised Learning