改进生成模型先验分布，提升少样本图像生成质量
Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis
- 通过调整训练时的先验分布，改善潜在编码匹配度
- 在9个少样本数据集上生成图像质量显著优于现有方法
- 适合研究少样本生成模型与扩散/对抗生成的开发者
当前研究致力于在数据量极少的情况下学习深度生成模型。传统生成模型如GAN和扩散模型需要大量数据才能表现良好，而在小样本训练时性能明显下降。近期提出的隐式最大似然估计（IMLE）已被适配至少样本场景，达到当前最优水平。然而，现有基于IMLE的方法存在训练时选取的潜在代码与推理时采样代码对应关系不足的问题，导致测试阶段性能不佳。本文从理论上提出解决方案，提出新方法RS-IMLE，通过改变训练时使用的先验分布，显著提升图像生成质量。在九个少样本图像数据集上的全面实验验证了该方法的有效性。
原文摘要 · Abstract (English)
An emerging area of research aims to learn deep generative models with limited training data. Prior generative models like GANs and diffusion models require a lot of data to perform well, and their performance degrades when they are trained on only a small amount of data. A recent technique called Implicit Maximum Likelihood Estimation (IMLE) has been adapted to the few-shot setting, achieving state-of-the-art performance. However, current IMLE-based approaches encounter challenges due to inadequate correspondence between the latent codes selected for training and those drawn during inference. This results in suboptimal test-time performance. We theoretically show a way to address this issue and propose RS-IMLE, a novel approach that changes the prior distribution used for training. This leads to substantially higher quality image generation compared to existing GAN and IMLE-based methods, as validated by comprehensive experiments conducted on nine few-shot image datasets.
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