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.
Supplementary Material: zip
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