A Coefficient Makes SVRG Effective

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 α to control the strength and adjust it through a linear decay schedule. We name our method α-SVRG. Our results show α-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.