Abstract
Most popular optimizers for deep learning can be broadly categorized as
adaptive methods (e.g. Adam) and accelerated schemes (e.g. stochastic gradient
descent (SGD) with momentum). For many models such as convolutional neural
networks (CNNs), adaptive methods typically converge faster but generalize
worse compared to SGD; for complex settings such as generative adversarial
networks (GANs), adaptive methods are typically the default because of their
stability.We propose AdaBelief to simultaneously achieve three goals: fast
convergence as in adaptive methods, good generalization as in SGD, and training
stability. The intuition for AdaBelief is to adapt the stepsize according to
the "belief" in the current gradient direction. Viewing the exponential moving
average (EMA) of the noisy gradient as the prediction of the gradient at the
next time step, if the observed gradient greatly deviates from the prediction,
we distrust the current observation and take a small step; if the observed
gradient is close to the prediction, we trust it and take a large step. We
validate AdaBelief in extensive experiments, showing that it outperforms other
methods with fast convergence and high accuracy on image classification and
language modeling. Specifically, on ImageNet, AdaBelief achieves comparable
accuracy to SGD. Furthermore, in the training of a GAN on Cifar10, AdaBelief
demonstrates high stability and improves the quality of generated samples
compared to a well-tuned Adam optimizer. Code is available at
https://github.com/juntang-zhuang/Adabelief-Optimizer
Description
[2010.07468v5] AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
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