Up and down residual blocks for convolutional generative adversarial networks

Abstract

Most recent existing image generation methods have made great progress in creating high-quality images, mainly focusing on improving the generator or discriminator of convolutional generative adversarial networks (GANs). In this paper, we propose up and down residual blocks for convolutional GANs, dubbed upResBlock and downResBlock respectively. This structure is based on deconvolutions, strided convolutions, and residual blocks. With the upResBlock module for the generator of convolutional GANs, our method can further enhance the generative power of the feature extraction while synthesizing image details for the specified size. With the downResBlock module for discriminator combined with upResBlock for generator, the proposed method can speed up the back propagation of gradient and doesn't suffer from the vanishing or exploding gradients problems, generating more realistic images as well. Extensive experiments demonstrate that the proposed up and down residual blocks can help convolutional GANs in generating photo-realistic images. In addition, our method shows its universality for the improvement of existing methods.