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The plateau phenomenon, wherein the loss value stops decreasing during theprocess of learning, has been reported by various researchers. The phenomenonis actively inspected in the 1990s and found to be due to the fundamentalhierarchical structure of neural network models. Then the phenomenon has beenthought as inevitable. However, the phenomenon seldom occurs in the context ofrecent deep learning. There is a gap between theory and reality. In this paper,using statistical mechanical formulation, we clarified the relationship betweenthe plateau phenomenon and the statistical property of the data learned. It isshown that the data whose covariance has small and dispersed eigenvalues tendto make the plateau phenomenon inconspicuous.

Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis