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Many adaptive optimization methods have been proposed and used in deeplearning, in which Adam is regarded as the default algorithm and widely used inmany deep learning frameworks. Recently, many variants of Adam, such asAdabound, RAdam and Adabelief, have been proposed and show better performancethan Adam. However, these variants mainly focus on changing the stepsize bymaking differences on the gradient or the square of it. Motivated by the factthat suitable damping is important for the success of powerful second-orderoptimizers, we discuss the impact of the constant $\epsilon$ for Adam in thispaper. Surprisingly, we can obtain better performance than Adam simply changingthe position of $\epsilon$. Based on this finding, we propose a new variant ofAdam called EAdam, which doesn't need extra hyper-parameters or computationalcosts. We also discuss the relationships and differences between our method andAdam. Finally, we conduct extensive experiments on various popular tasks andmodels. Experimental results show that our method can bring significantimprovement compared with Adam. Our code is available athttps://github.com/yuanwei2019/EAdam-optimizer.

EAdam Optimizer: How $\epsilon$ Impact Adam