TransGAN: TwoPureTransformersCanMakeOne StrongGAN,andThatCanScaleUp

The recent explosive interest on transformers has suggested their potential to become powerful "universal" models for computer vision tasks, such as classification, detection, and segmentation. While those attempts mainly study the discriminativemodels, weexplore transformers onsome more notoriously difficult vision tasks, e.g., generative adversarial networks (GANs). Our goal is to conduct the first pilot study in building a GANcompletely free of convolutions, using only pure transformer-based architectures.