  Transfer learning of StyleGAN has recently shown great potential to solve
diverse tasks, especially in domain translation. Previous methods utilized a
source model by swapping or freezing weights during transfer learning, however,
they have limitations on visual quality and controlling source features. In
other words, they require additional models that are computationally demanding
and have restricted control steps that prevent a smooth transition. In this
paper, we propose a new approach to overcome these limitations. Instead of
swapping or freezing, we introduce a simple feature matching loss to improve
generation quality. In addition, to control the degree of source features, we
train a target model with the proposed strategy, FixNoise, to preserve the
source features only in a disentangled subspace of a target feature space.
Owing to the disentangled feature space, our method can smoothly control the
degree of the source features in a single model. Extensive experiments
demonstrate that the proposed method can generate more consistent and realistic
images than previous works.
