The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success. The applicability of these techniques to the hard non-convex optimization problems encountered during training of modern deep neural networks is an open problem. We show that naive application of the SVRG technique and related approaches fail, and explore why.
The SVRG method fails on modern deep learning problems This paper presents an analysis of SVRG style methods, showing that dropout, batch norm, data augmentation (random crop/rotation/translations) tend to increase bias and/or variance of the updates. This paper investigates the applicability of SVGD to modern neural networks and shows the naive application of SVGD typically fails.