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Generative Adversarial Networks (GANs) have been shown to producerealistically looking synthetic images with remarkable success, yet theirperformance seems less impressive when the training set is highly diverse. Inorder to provide a better fit to the target data distribution when the datasetincludes many different classes, we propose a variant of the basic GAN model,called Gaussian Mixture GAN (GM-GAN), where the probability distribution overthe latent space is a mixture of Gaussians. We also propose a supervisedvariant which is capable of conditional sample synthesis. In order to evaluatethe model's performance, we propose a new scoring method which separately takesinto account two (typically conflicting) measures - diversity vs. quality ofthe generated data. Through a series of empirical experiments, using bothsynthetic and real-world datasets, we quantitatively show that GM-GANsoutperform baselines, both when evaluated using the commonly used InceptionScore, and when evaluated using our own alternative scoring method. Inaddition, we qualitatively demonstrate how the \textit{unsupervised} variant ofGM-GAN tends to map latent vectors sampled from different Gaussians in thelatent space to samples of different classes in the data space. We show howthis phenomenon can be exploited for the task of unsupervised clustering, andprovide quantitative evaluation showing the superiority of our method for theunsupervised clustering of image datasets. Finally, we demonstrate a featurewhich further sets our model apart from other GAN models: the option to controlthe quality-diversity trade-off by altering, post-training, the probabilitydistribution of the latent space. This allows one to sample higher quality andlower diversity samples, or vice versa, according to one's needs.

Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images