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arXiv · 1908.06881

SDIT: Scalable and Diverse Cross-domain Image Translation

Yaxing Wang·Abel Gonzalez-Garcia·Joost van de Weijer·Luis Herranz
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Abstract

Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.

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Yaxing Wang, Abel Gonzalez-Garcia, Joost van de Weijer, Luis Herranz. 2019-08-19. SDIT: Scalable and Diverse Cross-domain Image Translation. https://arxiv.org/abs/1908.06881

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