Breaking the Cycle - Colleagues Are All You Need
Ori Nizan, Ayellet Tal
2020Year
17Top-tier citations
Abstract
This paper proposes a novel approach to performing image-to-image translation between unpaired domains. Rather than relying on a cycle constraint, our method takes advantage of collaboration between various GANs. This results in a multi-modal method, in which multiple optional and diverse images are produced for a given image. Our model addresses some of the shortcomings of classical GANs: (1) It is able to remove large objects, such as glasses. ( 2
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Install the CLIlune papers fulltext 8bf428b1-b51c-44fc-82e4-99596c7a1ad3Cited by top-tier papers17
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Builds on2
- U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image TranslationJunho Kim, Minjae Kim, Hyeonwoo Kang, Kwanghee LeeICLR 2020 · 632 citations
- Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and LocalizationMd Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh, Ruibin Feng et al.ICCV 2019 · 97 citations
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