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Adaptive gradient algorithm (AdaGrad) and its variants, such as RMSProp,Adam, AMSGrad, etc, have been widely used in deep learning. Although thesealgorithms are faster in the early phase of training, their generalizationperformance is often not as good as stochastic gradient descent (SGD). Hence, atrade-off method of transforming Adam to SGD after a certain iteration to gainthe merits of both algorithms is theoretically and practically significant. Tothat end, we propose a decreasing scaling transition scheme to achieve a smoothand stable transition from Adam to SGD, which is called DSTAdam. Theconvergence of the proposed DSTAdam is also proved in an online convex setting.Finally, the effectiveness of the DSTAdam is verified on the CIFAR-10/100datasets. Our implementation is available at:https://github.com/kunzeng/DSTAdam.

A decreasing scaling transition scheme from Adam to SGD