Few-shot image generation (FSIG) learns to generate diverse and high-fidelity
images from a target domain using a few (e.g., 10) reference samples. Existing
FSIG methods select, preserve and transfer prior knowledge from a source
generator (pretrained on a related domain) to learn the target generator. In
this work, we investigate an underexplored issue in FSIG, dubbed as
incompatible knowledge transfer, which would significantly degrade the
realisticness of synthetic samples. Empirical observations show that the issue
stems from the least significant filters from the source generator. To this
end, we propose knowledge truncation to mitigate this issue in FSIG, which is a
complementary operation to knowledge preservation and is implemented by a
lightweight pruning-based method. Extensive experiments show that knowledge
truncation is simple and effective, consistently achieving state-of-the-art
performance, including challenging setups where the source and target domains
are more distant. Project Page: yunqing-me.github.io/RICK.

本研究提出了一种名为知识截断的方法来缓解 few-shot 图像生成中特有的不兼容知识转移问题，该方法是一种轻量级基于修剪的方法，旨在剪枝源生成器中的不重要的过滤器以加快培训速度，在各种挑战性的设置中实现了最先进的性能提升。