SGDF: A Method for Reducing Variance in Stochastic Gradient Descent via Filter Estimation

Zhipeng Yao, Guiyuan Fu, Ying Li, Yu Zhang, Dazhou Li, Rui Yu

Keywords: Stochastic Gradient Descent, Gradient Estimation, Generalization

TL;DR: This paper introduces SGDF that combines Wiener filtering with stochastic gradient descent, enhancing deep learning optimization by balancing convergence speed and model generalization.

Abstract: In deep learning, stochastic gradient descent (SGD) and its momentum-based variants are widely used for optimization, but they typically suffer from slow convergence. Conversely, existing adaptive learning rate optimizers speed up convergence but often compromise generalization. To resolve this issue, we propose a novel optimization method designed to accelerate SGD's convergence without sacrificing generalization. Our approach reduces the variance of the historical gradient, improves first-order moment estimation of SGD by applying Wiener filter theory, and introduces a time-varying adaptive gain. Empirical results demonstrate that SGDF (SGD with Filter) effectively balances convergence and generalization compared to state-of-the-art optimizers.

Primary Area: optimization

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Submission Number: 4781