Performance Comparison of CycleGAN for Image-to-Image Translation in Multiple Domains

This study conducts a comprehensive analysis of the performance of Cycle-Consistent Adversarial Networks (CycleGAN) across four distinct unpaired image-to-image translation tasks: summer-to-winter scenery conversion, apple-to-orange transformation, apple-to-guava transformation, and celebrity-to-comic style transfer. The primary focus is on enhancing CycleGAN’s loss function to achieve more accurate and detailed image translations. Using a dataset comprising both self-collected and publicly available images, our experiments demonstrate that the Improved CycleGAN consistently outperforms the original model, producing translations with superior detail and fidelity. Performance evaluation was conducted using quantitative metrics, including FID and Kernel Inception Distance (KID), alongside qualitative visual analysis. Results reveal a varying range of success across tasks, with FID scores ranging from X for seasonal conversion to Y for style transfer. The findings underscore domain-specific challenges and offer insights into the model’s strengths and limitations in diverse applications. By leveraging PyTorch for implementation, this study contributes to advancing high-quality image-to-image translation techniques and provides a deeper understanding of CycleGAN’s potential across multiple domains.