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We propose a GAN design which models multiple distributions effectively anddiscovers their commonalities and particularities. Each data distribution ismodeled with a mixture of $K$ generator distributions. As the generators arepartially shared between the modeling of different true data distributions,shared ones captures the commonality of the distributions, while non-sharedones capture unique aspects of them. We show the effectiveness of our method onvarious datasets (MNIST, Fashion MNIST, CIFAR-10, Omniglot, CelebA) withcompelling results.

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions