English(EN) Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory

新框架Controlled-Minimax稳定对抗性优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了一个名为Controlled-Minimax的新框架，以解决对抗性优化固有的稳定性问题。通过将训练动力学建模为二阶常微分方程（ODE），该方法识别出源于Hessian特征结构的失稳。Controlled-Minimax引入了一个反馈控制器，理论上保证了局部指数稳定性，从而在Dirac-GAN和StyleGAN2-ada等具有挑战性的基准上实现更快、更鲁棒的收敛。 AI

影响为稳定对抗性训练提供了理论保证，有望提高复杂生成模型的性能。

排序理由该集群包含一篇研究论文，详细介绍了一种稳定对抗性优化新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Tianjiao Luo, Jianfei Chen, Jun Zhu · 2026-10-12 04:00

Controlled-Minimax：通过控制理论稳定对抗性优化

arXiv:2610.12915v1 Announce Type: new Abstract: Minimax optimization is fundamental to adversarial learning yet suffers from severe stability challenges, often manifesting as persistent limit cycles or divergence. This paper investigates these instabilities through the lens of co…

本文如何被排名

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Controlled-Minimax：通过控制理论稳定对抗性优化

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