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arXiv · 2102.04699

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

Rajiv Kumar·Rishabh Dabral·G. Sivakumar
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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.

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Rajiv Kumar, Rishabh Dabral, G. Sivakumar. 2021-02-09. Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator. https://doi.org/10.5220/0010184102560264

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