  Generative Adversarial Networks (GANs) are a powerful class of generative
models. Despite their successes, the most appropriate choice of a GAN network
architecture is still not well understood. GAN models for image synthesis have
adopted a deep convolutional network architecture, which eliminates or
minimizes the use of fully connected and pooling layers in favor of convolution
layers in the generator and discriminator of GANs. In this paper, we
demonstrate that a convolution network architecture utilizing deep fully
connected layers and pooling layers can be more effective than the traditional
convolution-only architecture, and we propose FCC-GAN, a fully connected and
convolutional GAN architecture. Models based on our FCC-GAN architecture learn
both faster than the conventional architecture and also generate higher quality
of samples. We demonstrate the effectiveness and stability of our approach
across four popular image datasets.
