mlsanityData to 7 Oct 2026

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

arXiv 2102.04699 ↗First posted 9 Feb 2021cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 44 of its 46 references that are in the map. First posted on arXiv 9 Feb 2021.

Builds on

44 of 46 references.

References · 44

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32 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2.

4 papers.

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  3. 2023DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesiscited by 1
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