Stochastic Training is Not Necessary for Generalization

Jonas Geiping, Micah Goldblum, Phil Pope, Michael Moeller, Tom Goldstein

Keywords: Optimization, Generalization, Stochasticity, SGD, full-batch, implicit regularization, implicit bias

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.

One-sentence Summary: Models trained with full-batch gradient descent and explicit regularization can match the generalization performance of models trained with stochastic minibatching.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2109.14119/code)