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Data Augmentation Based on Scaling for Generative Models and Its Theoretical Extension

💡 原文英文，约100词，阅读约需1分钟。
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内容提要

本研究提出了Scale-GAN算法，解决了生成模型学习中的稳定性问题。通过数据缩放和正则化，实现高质量数据生成，并证明数据缩放能有效控制估计误差。

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标签

Scale-GAN models 估计误差 数据缩放 正则化 生成模型
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