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StarGAN

StarGAN

A framework to for multi-domain image-to-image translation using a single model

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Melanie Manipula
Melanie Manipula edited on 25 Jan 2018 1:43 am
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Article (+564 characters)
Academic papers
Documentaries, videos and podcasts
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StartGAN StarGAN

A framework to for multi-domain image-to-image translation using a single model

Article

StarGAN is a generative adversarial network capable of learning mappings among multiple domains. It has a unified modeling architecture that allows simultaneous training of multiple datasets and different domains within a single network.



StarGAN is a novel and scalable approach to perform image-to-image translation among multiple

domains using a single model. It can translate input images to any target domain. It can generate higher visual quality images compared to existing methods. It can perform facial attribute transfer and facial expression synthesis.



Academic papers

Author
Title
Link

Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim and Jaegul Choo

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Documentaries, videos and podcasts

Title
Date
Link

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

26 November 2017