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Recently, generative adversarial networks (GANs) have shown promisingperformance in generating realistic images. However, they often struggle inlearning complex underlying modalities in a given dataset, resulting inpoor-quality generated images. To mitigate this problem, we present a novelapproach called mixture of experts GAN (MEGAN), an ensemble approach ofmultiple generator networks. Each generator network in MEGAN specializes ingenerating images with a particular subset of modalities, e.g., an image class.Instead of incorporating a separate step of handcrafted clustering of multiplemodalities, our proposed model is trained through an end-to-end learning ofmultiple generators via gating networks, which is responsible for choosing theappropriate generator network for a given condition. We adopt the categoricalreparameterization trick for a categorical decision to be made in selecting agenerator while maintaining the flow of the gradients. We demonstrate thatindividual generators learn different and salient subparts of the data andachieve a multiscale structural similarity (MS-SSIM) score of 0.2470 for CelebAand a competitive unsupervised inception score of 8.33 in CIFAR-10.

MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation