  Few-shot image generation (FSIG) aims to learn to generate new and diverse
images given few (e.g., 10) training samples. Recent work has addressed FSIG by
leveraging a GAN pre-trained on a large-scale source domain and adapting it to
the target domain with few target samples. Central to recent FSIG methods are
knowledge preservation criteria, which select and preserve a subset of source
knowledge to the adapted model. However, a major limitation of existing methods
is that their knowledge preserving criteria consider only source domain/task
and fail to consider target domain/adaptation in selecting source knowledge,
casting doubt on their suitability for setups of different proximity between
source and target domain. Our work makes two contributions. Firstly, we revisit
recent FSIG works and their experiments. We reveal that under setups which
assumption of close proximity between source and target domains is relaxed,
many existing state-of-the-art (SOTA) methods which consider only source domain
in knowledge preserving perform no better than a baseline method. As our second
contribution, we propose Adaptation-Aware kernel Modulation (AdAM) for general
FSIG of different source-target domain proximity. Extensive experiments show
that AdAM consistently achieves SOTA performance in FSIG, including challenging
setups where source and target domains are more apart.
