Breaking the Cycle - Colleagues Are All You Need
Ori Nizan, Ayellet Tal
2020年份
17顶会引用
摘要
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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引用它的顶会 Paper17
- GAN-Control: Explicitly Controllable GANsAlon Shoshan, Nadav Bhonker, Igor Kviatkovsky, Gérard G. MedioniICCV 2021 · 被引用 151 次
- Pastiche Master: Exemplar-Based High-Resolution Portrait Style TransferShuai Yang, Liming Jiang, Ziwei Liu, Chen Change LoyCVPR 2022 · 被引用 130 次
- Wavelet Knowledge Distillation: Towards Efficient Image-to-Image TranslationLinfeng Zhang, Xin Chen, Xiaobing Tu, Pengfei Wan 等CVPR 2022 · 被引用 105 次
- QS-Attn: Query-Selected Attention for Contrastive Learning in I2I TranslationXueqi Hu, Xinyue Zhou, Qiusheng Huang, Zhengyi Shi 等CVPR 2022 · 被引用 103 次
- Learning to generate line drawings that convey geometry and semanticsCaroline Chan, Frédo Durand, Phillip IsolaCVPR 2022 · 被引用 86 次
它引用的顶会 Paper2
- 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 · 被引用 632 次
- 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 等ICCV 2019 · 被引用 97 次
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