Synthetic training data has gained prominence in numerous learning tasks and scenarios, offering advantages such as dataset augmentation, generalization evaluation, and privacy preservation. Despite these benefits, the efficiency of synthetic data generated by current methodologies remains inferior when training advanced deep models exclusively, limiting its practical utility. To address this challenge, we analyze the principles underlying training data synthesis for supervised learning and elucidate a principled theoretical framework from the distribution-matching perspective that explicates the mechanisms governing synthesis efficacy. Through extensive experiments, we demonstrate the effectiveness of our synthetic data across diverse image classification tasks, both as a replacement for and augmentation to real datasets, while also benefits such as out-of-distribution generalization, privacy preservation, and scalability. Specifically, we achieve 70.9% top1 classification accuracy on ImageNet1K when training solely with synthetic data equivalent to 1 X the original real data size, which increases to 76.0% when scaling up to 10 X synthetic data.

翻译：合成训练数据已在众多学习任务和场景中崭露头角，其优势包括数据集增强、泛化评估和隐私保护。尽管具有这些优势，当前方法生成的合成数据在单独训练先进深度模型时效率仍显不足，限制了其实用价值。为应对这一挑战，我们从分布匹配视角出发，系统分析了监督学习场景下训练数据合成的基本原理，阐释了一个具有理论依据的框架，揭示了合成效能的作用机制。通过大量实验，我们验证了合成数据在多种图像分类任务中的有效性——既能完全替代真实数据集，也可作为其增强补充，同时具备分布外泛化、隐私保护和可扩展性等优势。具体而言，仅使用与原始真实数据等量（1倍）的合成数据训练时，在ImageNet1K上实现了70.9%的Top1分类准确率；当合成数据规模扩大至10倍时，该指标提升至76.0%。