Unpaired Image-To-Image Translation Using Transformer-Based CycleGAN

The use of transformer-based architectures by computer vision researchers is on the rise. Recently, the implementations of GANs that use transformer-based architectures, such as TransGAN and ViTGAN, have demonstrated profitability for visual generative modeling. We introduced TransCycleGAN, a novel, efficient GAN model, and explored its application to image-to-image translation. In distinction to the architectures above, our generator utilizes source images as input, not simply noise. We developed it and carried out preliminary experiments on the horse2zebra resized to \(64 \times 64\) . The experimental outcomes show the potential of our new architecture. An implementation of the model is available under the MIT license at GitHub.