The power of DNNs depends heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often costly and time-consuming, which severely hinders the application of DNNs. To address this issue, we explore a new task, termed as dataset expansion, which seeks to expand a ready-to-use small dataset by automatically creating new labeled samples. To this end, we present a Guided Imagination Framework (GIF) that leverages cutting-edge generative models (e.g., DALL-E2, Stable Diffusion (SD)) to ``imagine'' and create informative new data from the input seed data. Specifically, GIF conducts data imagination by optimizing the latent features of the seed data in the semantically meaningful space of the prior model, which are used to create photo-realistic images with new content. To guide the imagination towards creating informative samples for model training, we introduce two key criteria, i.e., class-maintained information boosting and sample diversity promotion. The two criteria are verified to be essential for effective dataset expansion: GIF-SD obtains 13.5\% higher model accuracy on natural image datasets than unguided expansion with SD. With these essential criteria, GIF expands datasets effectively in various small-data scenarios, boosting model accuracy by 36.9\% on average over six natural image datasets and by 13.5\% on average over three medical datasets. The source code will be released: \url{https://github.com/Vanint/DatasetExpansion}.

翻译：深度神经网络的性能高度依赖于训练数据的数量和质量。然而，大规模收集和标注数据通常成本高昂且耗时，这严重制约了深度神经网络的应用。为解决这一问题，我们探索了一项新任务，即数据集扩展，旨在通过自动生成新的标注样本来扩展现有小型数据集。为此，我们提出了一种引导想象框架（GIF），该框架利用尖端生成模型（如DALL-E2、稳定扩散模型（SD））从输入种子数据中“想象”并生成信息丰富的新数据。具体而言，GIF通过优化种子数据在先验模型语义有意义空间中的潜在特征来执行数据想象，从而生成具有新内容的光照真实图像。为引导想象过程生成对模型训练有益的样本，我们引入两个关键准则：保持类别信息增强与促进样本多样性。这两个准则被验证对有效数据集扩展至关重要：在自然图像数据集上，GIF-SD相比使用SD进行无引导扩展，模型准确率提升13.5%。借助这些基本准则，GIF在各种小数据场景下有效扩展数据集，在六个自然图像数据集上平均提升模型准确率36.9%，在三个医学数据集上平均提升13.5%。源代码将公开发布：\url{https://github.com/Vanint/DatasetExpansion}。