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Recent studies show strong generative performance in domain translationespecially by using transfer learning techniques on the unconditionalgenerator. However, the control between different domain features using asingle model is still challenging. Existing methods often require additionalmodels, which is computationally demanding and leads to unsatisfactory visualquality. In addition, they have restricted control steps, which prevents asmooth transition. In this paper, we propose a new approach for high-qualitydomain translation with better controllability. The key idea is to preservesource features within a disentangled subspace of a target feature space. Thisallows our method to smoothly control the degree to which it preserves sourcefeatures while generating images from an entirely new domain using only asingle model. Our extensive experiments show that the proposed method canproduce more consistent and realistic images than previous works and maintainprecise controllability over different levels of transformation. The code isavailable at https://github.com/LeeDongYeun/FixNoise.

Fix the Noise: Disentangling Source Feature for Controllable Domain Translation