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Generative moment matching networks (GMMN) present a theoretically sound approach to learning deep generative models. However, such methods are typically limited by the high sample complexity, thereby impractical in generating complex data. In this paper, we present a new strategy to train GMMN with a low sample complexity while retaining the theoretical soundness. Our method introduces some auxiliary variables, whose values are provided by a pre-trained model such as an encoder network indblp:conf/aaai/RenLZ21 fatcat:rec2etvyzvfpfjdczhph3hsllq