  Thanks to the recent development of deep generative models, it is becoming
possible to generate high-quality images with both fidelity and diversity.
However, the training of such generative models requires a large dataset. To
reduce the amount of data required, we propose a new method for transferring
prior knowledge of the pre-trained generator, which is trained with a large
dataset, to a small dataset in a different domain. Using such prior knowledge,
the model can generate images leveraging some common sense that cannot be
acquired from a small dataset. In this work, we propose a novel method focusing
on the parameters for batch statistics, scale and shift, of the hidden layers
in the generator. By training only these parameters in a supervised manner, we
achieved stable training of the generator, and our method can generate higher
quality images compared to previous methods without collapsing, even when the
dataset is small (~100). Our results show that the diversity of the filters
acquired in the pre-trained generator is important for the performance on the
target domain. Our method makes it possible to add a new class or domain to a
pre-trained generator without disturbing the performance on the original
domain.
