当漂亮并不有用：调查现代文本到图像模型为何无法作为可靠的训练数据生成器
When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators
AI总结：
研究发现现代文本到图像模型在生成合成数据时性能下降，揭示其生成的数据分布狭窄且缺乏多样性，挑战了生成现实感进步即意味着数据现实感进步的假设。
摘要：
近期文本到图像（T2I）扩散模型生成的图像视觉效果出色，能很好地遵循提示。但它们作为合成视觉数据生成器表现如何？本文重新审视合成数据作为真实训练集替代品的潜力，发现令人惊讶的性能下降。我们使用2022至2025年发布的最新T2I模型生成大规模合成数据集，仅用此数据训练标准分类器，并在真实测试数据上评估其性能。尽管在视觉真实性和提示遵循上有所进步，但分类准确率在真实测试数据上随着训练数据生成器的更新而持续下降。我们的分析揭示了一个隐藏趋势：这些模型倾向于一种狭窄的、以审美为中心的分布，这会削弱多样性和对真实数据分布的覆盖。总体而言，我们的发现挑战了视觉研究中一个日益增长的假设，即生成现实感的进步意味着数据现实感的进步。因此，我们强调亟需重新思考现代T2I模型作为可靠训练数据生成器的能力。
英文摘要：
Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as synthetic vision data generators? In this work, we revisit the promise of synthetic data as a scalable substitute for real training sets and uncover a surprising performance regression. We generate large-scale synthetic datasets using state-of-the-art T2I models released between 2022 and 2025, train standard classifiers solely on this synthetic data, and evaluate them on real test data. Despite observable advances in visual fidelity and prompt adherence, classification accuracy on real test data consistently declines with newer T2I models as training data generators. Our analysis reveals a hidden trend: These models collapse to a narrow, aesthetic-centric distribution that undermines diversity and real data distribution coverage. Overall, our findings challenge a growing assumption in vision research, namely that progress in generative realism implies progress in data realism. We thus highlight an urgent need to rethink the capabilities of modern T2I models as reliable training data generators.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
