Generative Adversarial Networks (GANs) are powerful tools for realistic image generation. However, a major drawback of GANs is that they are especially hard to train, often requiring large amounts of data and long training time. In this paper we propose the Deli-Fisher GAN, a GAN that generates photo-realistic images by enforcing structure on the latent generative space using similar approaches in \cite{deligan}. The structure of the latent space we consider in this paper is modeled as a mixture of Gaussians, whose parameters are learned in the training process. Furthermore, to improve stability and efficiency, we use the Fisher Integral Probability Metric as the divergence measure in our GAN model, instead of the Jensen-Shannon divergence. We show by experiments that the Deli-Fisher GAN performs better than DCGAN, WGAN, and the Fisher GAN as measured by inception score. Generative Adversarial Networks (GAN) are powerful unsupervised learning models that have recently achieved great success in learning high-dimensional distributions BID1 ). In the field of image and vision sciences in particular, GAN models are capable of generating "fake" images that look authentic to human observers.The basic framework of a GAN model consists of two parts: a generator G = G θ (z) that generates images by translating random input noise z into a particular distribution of interest, and a discriminator D = D p (x) which calculates the probability that an image x is an authentic image as opposed to a generated "fake" image from the generator. While the generator G and discriminator D can be modeled as any smooth functions, these two components are usually modeled as two neural networks in practical applications. During the training process, we optimize the generator and the discriminator alternately against each other. Within each step, we first keep D fixed and optimize G so as to improve its capability of generating images that look real to D. Then, we keep G fixed and train D to improve the discriminator's ability to distinguish real and G-generated images. The two parts G and D play a two-player game against each other. At the end of the training, we would be able to have a generator that is capable of generating photo-realistic images.In mathematical form, a GAN model can be described as an optimization problem, as follows: DISPLAYFORM0 where V (D, G) is the objective function measuring the divergence between the two distributions: the distribution of the real existing data D(x), and the that of the generated data D(G(z)), where x follows the distribution of real images and z follows the distribution of input noise. Depending on the choice of function V (D, G), different GAN models have been proposed over time (see BID1 , , BID4 ) to increase stability and achieve faster convergence rates.
This paper proposes a new Generative Adversarial Network that is more stable, more efficient, and produces better images than those of status-quo  This paper combines Fisher-GAN and Deli-GAN This paper combines Deli-GAN, which has a mixture prior distribution in latent space, and Fisher GAN, which uses Fisher IPM instead of JSD as an objective.