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May, 2023
用于训练生成对抗网络的梯度下降-上升的本地收敛性
Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks
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Evan Becker, Parthe Pandit, Sundeep Rangan, Alyson K. Fletcher
TL;DR
研究了使用基于核的判别器训练生成式对抗网络的梯度下降-上升过程,通过线性化的非线性动态系统描述方法,探究了学习率、正则化和核判别器带宽对该过程的局部收敛速度的影响,提出了系统收敛、振荡和发散的阶段转换点,并通过数值模拟验证了结论。
Abstract
generative adversarial networks
(GANs) are a popular formulation to train generative models for complex high dimensional data. The standard method for training GANs involves a
gradient descent-ascent
(GDA) proced
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