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Unsupervised multi-domain image-to-image translation aims to synthesis imagesamong multiple domains without labeled data, which is more general andcomplicated than one-to-one image mapping. However, existing methods mainlyfocus on reducing the large costs of modeling and do not pay enough attentionto the quality of generated images. In some target domains, their translationresults may not be expected or even it has model collapse. To improve the imagequality, we propose an effective many-to-many mapping framework forunsupervised multi-domain image-to-image translation. There are two key aspectsin our method. The first is a proposed many-to-many architecture with only onedomain-shared encoder and several domain-specialized decoders to effectivelyand simultaneously translate images across multiple domains. The second is twoproposed constraints extended from one-to-one mappings to further help improvethe generation. All the evaluations demonstrate our framework is superior toexisting methods and provides an effective solution for multi-domainimage-to-image translation.

Unsupervised Many-to-Many Image-to-Image Translation Across Multiple Domains