Real-Fake: Effective Training Data Synthesis Through Distribution Matching
This addresses the practical utility of synthetic data for tasks like dataset augmentation and privacy preservation, showing incremental improvements in efficiency.
The paper tackles the problem of inefficient synthetic training data for deep models by proposing a distribution-matching framework, achieving 70.9% top1 accuracy on ImageNet1K with synthetic data equivalent to the original size and 76.0% with 10x scaling.
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.