Generative adversarial networks in computer vision: image synthesis and manipulation

Abstract

Abstract In this paper, we first use the hidden variable loss to generate an adversarial network to optimize the efficiency of the original GAN operation. Secondly, the optimized GAN algorithm is used to train the encoder and decoder to form a new image-processing GAN by combining with the self-encoder and to standardize the operation process of image synthesis. On this basis, the performance of the self-encoder GAN is compared with the original GAN algorithm, and the quality of images generated by the self-encoder GAN and other ways is tested. The test results show that compared with the original GAN framework, the self-encoder GAN has a 50% improvement in operational efficiency in terms of root-mean-square error and an 80% reduction in synthesized global relative error. The self-coder GAN based on this algorithm is 22.5% higher than the traditional BiGAN framework and 2.5% higher than the OGAN framework in terms of FID quality criteria of the generated images and produces 30% less data capacity than BiGAN. The generated images have an average IS score of 3.435, which is superior to other base algorithms.