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