  Generative Adversarial Networks (GANs) have been shown to produce
realistically looking synthetic images with remarkable success, yet their
performance seems less impressive when the training set is highly diverse. In
order to provide a better fit to the target data distribution when the dataset
includes many different classes, we propose a variant of the basic GAN model,
called Gaussian Mixture GAN (GM-GAN), where the probability distribution over
the latent space is a mixture of Gaussians. We also propose a supervised
variant which is capable of conditional sample synthesis. In order to evaluate
the model's performance, we propose a new scoring method which separately takes
into account two (typically conflicting) measures - diversity vs. quality of
the generated data. Through a series of empirical experiments, using both
synthetic and real-world datasets, we quantitatively show that GM-GANs
outperform baselines, both when evaluated using the commonly used Inception
Score, and when evaluated using our own alternative scoring method. In
addition, we qualitatively demonstrate how the \textit{unsupervised} variant of
GM-GAN tends to map latent vectors sampled from different Gaussians in the
latent space to samples of different classes in the data space. We show how
this phenomenon can be exploited for the task of unsupervised clustering, and
provide quantitative evaluation showing the superiority of our method for the
unsupervised clustering of image datasets. Finally, we demonstrate a feature
which further sets our model apart from other GAN models: the option to control
the quality-diversity trade-off by altering, post-training, the probability
distribution of the latent space. This allows one to sample higher quality and
lower diversity samples, or vice versa, according to one's needs.
