LiWGAN: A Light Method to Improve the Performance of Generative Adversarial Network

Abstract

Generative adversarial networks (GANs) gained tremendous growth due to the potency and efficiency in producing realistic samples. This study proposes a light-weight GAN (LiWGAN) to learn non-image synthesis with minimum computational time for less power computing. Hence, the LiWGAN method enhanced a new skip-layer channel-wise excitation module (SLE) and a self-supervised discriminator design for non-synthesis performance using the facemask dataset. Facemask is one of the preventative strategies pioneered by the current COVID-19 pandemic. LiWGAN manipulates a non-image synthesis of facemasks that could be beneficial for some researchers to identify an individual using lower power devices, occlusion challenges for face recognition, and alleviate the accuracy challenges due to limited datasets. The study evaluates the performance of the processing time in terms of batch sizes and image resolutions using the facemask dataset. The Fréchet inception distance (FID) was also measured on the facemask images to evaluate the quality of the augmented image using LiWGAN. The findings for 3000 generated images showed a nearly similar FID score at 220.43 with significantly less processing time per iteration at 1.03s than StyleGAN at 219.97 FID score. One experiment was conducted using the CelebA dataset to compare with GL-GAN and DRAGAN, proving LiWGAN is appropriate for other datasets. The outcomes found LiWGAN performed better than GL-GAN and DRAGAN at 91.31 FID score with 3.50s processing time per iteration. Therefore, LiWGAN could aim to enhance the FID score to be near zero in the future with less processing time by using different datasets.

Keywords

• Non-image synthesis
• self-supervised discriminator
• data augmentation
• deep learning
• generative adversarial network