VitGAN.pdf (811.05 kB)
Download fileVit-GAN: Image-to-image Translation with Vision Transformes and Conditional GANS
In this paper, we have developed a general-purpose architecture, Vit-Gan, capable of performing
most of the image-to-image translation tasks from semantic image segmentation to single image depth
perception. This paper is a follow-up paper, an extension of generator based model [1] in which the
obtained results were very promising. This opened the possibility of further improvements with adversarial architecture. We used a unique vision transformers-based generator architecture and Conditional
GANs(cGANs) with a Markovian Discriminator (PatchGAN) (https://github.com/YigitGunduc/vit-gan).
In the present work, we use images as conditioning arguments. It is observed that the obtained results
are more realistic than the commonly used architectures.
History
Email Address of Submitting Author
ygunduc@gmail.comORCID of Submitting Author
https://orcid.org/0000-0001-5210-4088Submitting Author's Institution
Ankara University Development Foundation Private High SchoolSubmitting Author's Country
- Turkey