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Vit-GAN: Image-to-image Translation with Vision Transformes and Conditional GANS

arXiv 2110.09305 ↗First posted 11 Oct 2021eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 11 Oct 2021.

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References · 19

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7 more
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  7. 2021Tensor-to-Image: Image-to-Image Translation with Vision Transformerscited by 1

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