Enhancing Symmetry in GAN Generated Fashion Images

Vishnu Makkapati, Arun Patro

Abstract: Generative adversarial networks (GANs) are being used in several fields to produce new images that are similar to those in the input set. We train a GAN to generate images of articles pertaining to fashion that have inherent horizontal symmetry in most cases. Variants of GAN proposed so far do not exploit symmetry and hence may or may not produce fashion designs that are realistic. We propose two methods to exploit symmetry, leading to better designs - (a) Introduce a new loss to check if the flipped version of the generated image is equivalently classified by the discriminator (b) Invert the flipped version of the generated image to reconstruct an image with minimal distortions. We present experimental results to show that imposing the new symmetry loss produces better looking images and also reduces the training time.