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.

本文提出了一种基于对抗生成网络的模型 GM-GAN，其中对概率分布进行了控制，使得模型在高维度多样化数据集上的表现得以提高，并在合成图像和真实数据集上进行了多次实验证明了该模型的性能等方面的优势。