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PACL: Piecewise Arc Cotangent Decay Learning Rate For Deep Neural Network Training
2020
IEEE Access
Deep neural networks (DNNs) are currently the best-performing method for many classification problems. For training DNNs, the learning rate is the most important hyper-parameter, choice of which affects the performance of the model greatly. In recent years, some learning rate schedulers, such as HTD, CLR, and SGDR, have been proposed. These methods, some of which make use of the cycling mechanism to improve the convergence speed and accuracy of DNN, but performance degradation occurs in the
doi:10.1109/access.2020.3002884
fatcat:ylf2yyp6cnfhbiuqyqstz7gxxq