Denoising diffusion probabilistic models (DDPMs) have been proven capable of synthesizing high-quality images with remarkable diversity when trained on large amounts of data. However, to our knowledge, few-shot image generation tasks have yet to be studied with DDPM-based approaches. Modern approaches are mainly built on Generative Adversarial Networks (GANs) and adapt models pre-trained on large source domains to target domains using a few available samples. In this paper, we make the first attempt to study when do DDPMs overfit and suffer severe diversity degradation as training data become scarce. Then we fine-tune DDPMs pre-trained on large source domains on limited target data directly. Our results show that utilizing knowledge from pre-trained models can accelerate convergence and improve generation quality and diversity compared with training from scratch. However, the fine-tuned models still fail to retain some diverse features and can only achieve limited diversity. Therefore, we propose a pairwise DDPM adaptation (DDPM-PA) approach based on a pairwise similarity loss to preserve the relative distances between generated samples during domain adaptation. DDPM-PA further improves generation diversity and achieves results better than current state-of-the-art GAN-based approaches. We demonstrate the effectiveness of DDPM-PA on a series of few-shot image generation tasks qualitatively and quantitatively.

翻译：事实证明,在对大量数据进行培训时,能够将高质量的图像与显著多样性结合起来,并具有显著的多样性;然而,据我们所知,尚未采用基于DDPM的方法研究少量图像生成任务;现代方法主要建立在General Aversarial Networks(GANs)上,在大源域经预先培训后,利用少数现有样本将模型应用于目标领域。因此,我们首次尝试在DDPMs过度适应和遭受严重多样性退化时进行研究,因为培训数据变得稀缺。然后,我们先在大源域直接就有限目标数据对DDPMS进行微调培训。我们的结果显示,利用来自预先培训的模型的知识可以加快趋同,提高生成质量和多样性,而从零开始进行培训。然而,经过微调的模型仍然无法保留一些不同的特征,只能实现有限的多样性。因此,我们建议基于相近似性损失的DDPM(DPM-PA)适应方法,以保持所生成的样本之间的相对距离。