Stochastic Training is Not Necessary for Generalization
Authors: Jonas Geiping,Micah Goldblum,Phil Pope,Michael Moeller,Tom Goldstein
ICLR 2022,Poster

Abstract: It is widely believed that the implicit regularization of SGD is fundamental to the impressive generalization behavior we observe in neural networks.  In this work, we demonstrate that non-stochastic full-batch training can achieve comparably strong performance to SGD on CIFAR-10 using modern architectures. To this end, we show that the implicit regularization of SGD can be completely replaced with explicit regularization. Our observations indicate that the perceived difficulty of full-batch training may be the result of its optimization properties and the disproportionate time and effort spent by the ML community tuning optimizers and hyperparameters for small-batch training.