Image-to-image Translation Based on Improved Cycle-consistent Generative Adversarial Network

Abstract

Image-to-image translation is a method to convert images in different domains. With the rapid development of the Generative Adversarial Network(GAN) in deep learning, GAN applications are increasingly concerned in the field of image-to-image translation. However, classical algorithms have disadvantages that the paired training data is difficult to obtain and the convert effect of generation image is poor. An improved Cycle-consistent Generative Adversarial Network(CycleGAN++) is proposed. New algorithm removes the loop network, and cascades the prior information of the target domain and the source domain in the image generation stage, The loss function is optimized as well, using classification loss instead of cycle consistency loss, realizing image-to-image translation without training data mapping. The evaluation of experiments on the CelebA and Cityscapes dataset show that new method can reach higher precision under the two classical criteria—Amazon Mechanical Turk perceptual studies(AMT perceptual studies) and Full-Convolutional Network score(FCN score), than the classical algorithms such as CycleGAN, IcGAN, CoGAN, and DIAT.