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- W2896702065 abstract "State-of-the-art techniques in Generative Adversarial Networks (GANs) have shown remarkable success in image-to-image translation from peer domain X to domain Y using paired image data. However, obtaining abundant paired data is a non-trivial and expensive process in the majority of applications. When there is a need to translate images across n domains, if the training is performed between every two domains, the complexity of the training will increase quadratically. Moreover, training with data from two domains only at a time cannot benefit from data of other domains, which prevents the extraction of more useful features and hinders the progress of this research area. In this work, we propose a general framework for unsupervised image-to-image translation across multiple domains, which can translate images from domain X to any a domain without requiring direct training between the two domains involved in image translation. A byproduct of the framework is the reduction of computing time and computing resources since it needs less time than training the domains in pairs as is done in state-of-the-art works. Our proposed framework consists of a pair of encoders along with a pair of GANs which learns high-level features across different domains to generate diverse and realistic samples from. Our framework shows competing results on many image-to-image tasks compared with state-of-the-art techniques." @default.
- W2896702065 created "2018-10-26" @default.
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- W2896702065 date "2018-10-15" @default.
- W2896702065 modified "2023-10-16" @default.
- W2896702065 title "Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation" @default.
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- W2896702065 doi "https://doi.org/10.1145/3240508.3240716" @default.
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