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Learning rate is one of the most important hyper-parameters that has asignificant influence on neural network training. Learning rate schedules arewidely used in real practice to adjust the learning rate according topre-defined schedules for fast convergence and good generalization. However,existing learning rate schedules are all heuristic algorithms and lacktheoretical support. Therefore, people usually choose the learning rateschedules through multiple ad-hoc trials, and the obtained learning rateschedules are sub-optimal. To boost the performance of the obtained sub-optimallearning rate schedule, we propose a generic learning rate schedule plugin,called LEArning Rate Perturbation (LEAP), which can be applied to variouslearning rate schedules to improve the model training by introducing a certainperturbation to the learning rate. We found that, with such a simple yeteffective strategy, training processing exponentially favors flat minima ratherthan sharp minima with guaranteed convergence, which leads to bettergeneralization ability. In addition, we conduct extensive experiments whichshow that training with LEAP can improve the performance of various deeplearning models on diverse datasets using various learning rate schedules(including constant learning rate).

Learning Rate Perturbation: A Generic Plugin of Learning Rate Schedule towards Flatter Local Minima