SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder

21 Jun 2019Marek ŚmiejaMaciej WołczykJacek TaborBernhard C. Geiger

We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto-encoder framework. We choose a mixture of Gaussians as a target distribution in latent space, which provides a natural splitting of data into clusters... (read more)

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