Generative Autotransporters

06/08/2017

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by   Jiqing Wu, et al.

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In this paper, we aim to introduce the classic Optimal Transport theory to enhance deep generative probabilistic modeling. For this purpose, we design a Generative Autotransporter (GAT) model with explicit distribution optimal transport. Particularly, the GAT model owns a deep distribution transporter to transfer the target distribution to a specific prior probability distribution, which enables a regular decoder to generate target samples from the input data that follows the transported prior distribution. With such a design, the GAT model can be stably trained to generate novel data by merely using a very simple l_1 reconstruction loss function with a generalized manifold-based Adam training algorithm. The experiments on two standard benchmarks demonstrate its strong generation ability.