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We propose Mask CycleGAN, a novel architecture for unpaired image domaintranslation built based on CycleGAN, with an aim to address two issues: 1)unimodality in image translation and 2) lack of interpretability of latentvariables. Our innovation in the technical approach is comprised of three keycomponents: masking scheme, generator and objective. Experimental resultsdemonstrate that this architecture is capable of bringing variations togenerated images in a controllable manner and is reasonably robust to differentmasks.

Mask CycleGAN: Unpaired Multi-modal Domain Translation with Interpretable Latent Variable