  Transfer learning for GANs successfully improves generation performance under
low-shot regimes. However, existing studies show that the pretrained model
using a single benchmark dataset is not generalized to various target datasets.
More importantly, the pretrained model can be vulnerable to copyright or
privacy risks as membership inference attack advances. To resolve both issues,
we propose an effective and unbiased data synthesizer, namely Primitives-PS,
inspired by the generic characteristics of natural images. Specifically, we
utilize 1) the generic statistics on the frequency magnitude spectrum, 2) the
elementary shape (i.e., image composition via elementary shapes) for
representing the structure information, and 3) the existence of saliency as
prior. Since our synthesizer only considers the generic properties of natural
images, the single model pretrained on our dataset can be consistently
transferred to various target datasets, and even outperforms the previous
methods pretrained with the natural images in terms of Fr'echet inception
distance. Extensive analysis, ablation study, and evaluations demonstrate that
each component of our data synthesizer is effective, and provide insights on
the desirable nature of the pretrained model for the transferability of GANs.
