Acquiring high-quality data for training discriminative models is a crucial
yet challenging aspect of building effective predictive systems. In this paper,
we present Diffusion Inversion, a simple yet effective method that leverages
the pre-trained generative model, Stable Diffusion, to generate diverse,
high-quality training data for image classification. Our approach captures the
original data distribution and ensures data coverage by inverting images to the
latent space of Stable Diffusion, and generates diverse novel training images
by conditioning the generative model on noisy versions of these vectors. We
identify three key components that allow our generated images to successfully
supplant the original dataset, leading to a 2-3x enhancement in sample
complexity and a 6.5x decrease in sampling time. Moreover, our approach
consistently outperforms generic prompt-based steering methods and KNN
retrieval baseline across a wide range of datasets. Additionally, we
demonstrate the compatibility of our approach with widely-used data
augmentation techniques, as well as the reliability of the generated data in
supporting various neural architectures and enhancing few-shot learning.

本文介绍了一种名为 “Diffusion Inversion” 的方法，该方法使用预先训练的生成模型 Stable Diffusion，通过将图像倒换到其潜在空间中来捕获原始数据分布并确保数据覆盖，即可生成多样高质量的训练图片，此方法通过三个关键部件成功替换了原始数据集，从而提高了样本复杂度并缩短了采样时间，不仅优于泛化提示导向方法和 KNN 检索基线，还可与常见数据增强技术兼容，提高少样本学习的可靠性。