Few-shot image generation (FSIG) aims to learn to generate new and diverse samples given an extremely limited number of samples from a domain, e.g., 10 training samples. Recent work has addressed the problem using transfer learning approach, leveraging a GAN pretrained on a large-scale source domain dataset and adapting that model to the target domain based on very limited target domain samples. Central to recent FSIG methods are knowledge preserving criteria, which aim to select a subset of source model's knowledge to be preserved into the adapted model. However, a major limitation of existing methods is that their knowledge preserving criteria consider only source domain/source task, and they fail to consider target domain/adaptation task in selecting source model's knowledge, casting doubt on their suitability for setups of different proximity between source and target domain. Our work makes two contributions. As our first contribution, we re-visit recent FSIG works and their experiments. Our important finding is that, under setups which assumption of close proximity between source and target domains is relaxed, existing state-of-the-art (SOTA) methods which consider only source domain/source task in knowledge preserving perform no better than a baseline fine-tuning method. To address the limitation of existing methods, as our second contribution, we propose Adaptation-Aware kernel Modulation (AdAM) to address general FSIG of different source-target domain proximity. Extensive experimental results show that the proposed method consistently achieves SOTA performance across source/target domains of different proximity, including challenging setups when source and target domains are more apart. Project Page: https://yunqing-me.github.io/AdAM/

翻译：小样本图像生成（FSIG）旨在从领域中极有限的样本（例如10个训练样本）中学习生成新颖且多样的样本。近期工作采用迁移学习方法解决该问题，利用在大型源域数据集上预训练的生成对抗网络（GAN），并基于极少的目标域样本将该模型适应到目标域。当前FSIG方法的核心是知识保留准则，该准则旨在从源模型中选择需保留到适应模型中的子集知识。然而，现有方法的主要局限性在于其知识保留准则仅考虑源域/源任务，而未在源模型知识选择过程中考虑目标域/适应任务，这质疑了其在源域与目标域不同邻近程度场景下的适用性。本文做出两项贡献：作为第一项贡献，我们重新审视了近期FSIG工作及其实验。我们的重要发现是，在放宽源域与目标域高度邻近假设的设定下，现有仅考虑源域/源任务进行知识保留的最优方法（SOTA）表现并不优于微调基线方法。为解决现有方法的局限性，作为第二项贡献，我们提出适应性感知核调制（AdAM）方法，以应对源-目标域不同邻近程度的一般性FSIG任务。大量实验结果表明，所提方法在不同邻近程度的源/目标域（包括源域与目标域差异较大的挑战性设定）中均持续取得SOTA性能。项目页面：https://yunqing-me.github.io/AdAM/