  Stochastic Variance Reduced Gradient (SVRG), introduced by Johnson & Zhang
(2013), is a theoretically compelling optimization method. However, as Defazio
& Bottou (2019) highlights, its effectiveness in deep learning is yet to be
proven. In this work, we demonstrate the potential of SVRG in optimizing
real-world neural networks. Our analysis finds that, for deeper networks, the
strength of the variance reduction term in SVRG should be smaller and decrease
as training progresses. Inspired by this, we introduce a multiplicative
coefficient $\alpha$ to control the strength and adjust it through a linear
decay schedule. We name our method $\alpha$-SVRG. Our results show
$\alpha$-SVRG better optimizes neural networks, consistently reducing training
loss compared to both baseline and the standard SVRG across various
architectures and image classification datasets. We hope our findings encourage
further exploration into variance reduction techniques in deep learning. Code
is available at https://github.com/davidyyd/alpha-SVRG.
