  Few-shot image generation seeks to generate more data of a given domain, with
only few available training examples. As it is unreasonable to expect to fully
infer the distribution from just a few observations (e.g., emojis), we seek to
leverage a large, related source domain as pretraining (e.g., human faces).
Thus, we wish to preserve the diversity of the source domain, while adapting to
the appearance of the target. We adapt a pretrained model, without introducing
any additional parameters, to the few examples of the target domain. Crucially,
we regularize the changes of the weights during this adaptation, in order to
best preserve the information of the source dataset, while fitting the target.
We demonstrate the effectiveness of our algorithm by generating high-quality
results of different target domains, including those with extremely few
examples (e.g., <10). We also analyze the performance of our method with
respect to some important factors, such as the number of examples and the
dissimilarity between the source and target domain.
