Mixture Density Generative Adversarial Networks

CVPR 2019 Hamid Eghbal-zadehWerner ZellingerGerhard Widmer

Generative Adversarial Networks have surprising ability for generating sharp and realistic images, though they are known to suffer from the so-called mode collapse problem. In this paper, we propose a new GAN variant called Mixture Density GAN that while being capable of generating high-quality images, overcomes this problem by encouraging the Discriminator to form clusters in its embedding space, which in turn leads the Generator to exploit these and discover different modes in the data... (read more)

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