Reducing Bias in Deep Learning Optimization: The RSGDM Approach

TL;DR

This paper introduces RSGDM, a new optimization algorithm for deep learning that corrects bias and lag in gradient estimation, leading to improved convergence accuracy over traditional SGDM.

Contribution

The paper presents a novel RSGDM algorithm that corrects bias and lag in gradient estimates of SGDM using differential correction, enhancing optimization performance.

Findings

RSGDM outperforms SGDM in convergence accuracy on CIFAR datasets.

Bias and lag in exponential moving averages negatively impact optimization.

Differential correction effectively reduces bias and lag in gradient estimation.

Abstract

Currently, widely used first-order deep learning optimizers include non-adaptive learning rate optimizers and adaptive learning rate optimizers. The former is represented by SGDM (Stochastic Gradient Descent with Momentum), while the latter is represented by Adam. Both of these methods use exponential moving averages to estimate the overall gradient. However, estimating the overall gradient using exponential moving averages is biased and has a lag. This paper proposes an RSGDM algorithm based on differential correction. Our contributions are mainly threefold: 1) Analyze the bias and lag brought by the exponential moving average in the SGDM algorithm. 2) Use the differential estimation term to correct the bias and lag in the SGDM algorithm, proposing the RSGDM algorithm. 3) Experiments on the CIFAR datasets have proven that our RSGDM algorithm is superior to the SGDM algorithm in terms of convergence accuracy.