We propose a semi-supervised generative model, SeGMA, which learns joint probability distribution of data and their classes is implemented in typical Wasserstein autoencoder framework. choose mixture Gaussians as target latent space, provides natural splitting into clusters. To connect Gaussian components with correct classes, we use small amount labeled classifier induced by the distribution. ...