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Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
TL;DR: This work proposes a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN) and successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.
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Abstract: While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity. Source code for official implementation is publicly available this https URL
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MIXGAN: Learning Concepts from Different Domains for Mixture Generation
Guang-Yuan Hao,Hong-Xing Yu,Wei-Shi Zheng,Wei-Shi Zheng +3 more
- 01 Jul 2018
TL;DR: A mixture generative adversarial network (MIXGAN) is proposed, which learns concepts of content and style from two domains respectively, and thus can join them for mixture generation in a new domain, i.e., generating images with content from one domain andstyle from another.
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Towards Multi-pose Guided Virtual Try-on Network
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Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks
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Image-to-Image Translation with Conditional Adversarial Networks
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