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Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator

2021-02-09 08:26:23
Rajiv Kumar, Rishabh Dabral, G. Sivakumar
arXiv_CV
             
Abstract

Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.

Abstract (translated)

URL

https://arxiv.org/abs/2102.04699

PDF

https://arxiv.org/pdf/2102.04699.pdf

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