Analysis of cycle consistent generative adversarial networks for unpaired image translation

Abstract

Unpaired image translation has become an important research topic in computer vision due to the difficulty of obtaining paired datasets in real-world scenarios. Cycle consistent generative adversarial networks (CycleGAN) provide an effective solution by learning mappings between two image domains without paired supervision. This paper presents a comprehensive analysis of the CycleGAN model, including its architecture, training strategy, and key components such as cycle consistency and identity mapping mechanisms. Experiments are conducted on benchmark datasets, including horse-to-zebra and seasonal translation tasks, and evaluated using SSIM and PSNR metrics. The results demonstrate that CycleGAN can generate visually realistic images while effectively preserving the structural content and essential features of the input images. Compared with traditional supervised methods, it significantly reduces the reliance on labeled data while still achieving competitive performance in various scenarios. However, limitations such as insufficient semantic consistency, visual artifacts in complex regions, and limited controllability are also discussed in detail, indicating directions for future improvement.