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Deep neural networks, which employ batch normalization and ReLU-likeactivation functions, suffer from instability in the early stages of trainingdue to the high gradient induced by temporal gradient explosion. In this study,we analyze the occurrence and mitigation of gradient explosion boththeoretically and empirically, and discover that the correlation betweenactivations plays a key role in preventing the gradient explosion frompersisting throughout the training. Finally, based on our observations, wepropose an improved adaptive learning rate algorithm to effectively control thetraining instability.

The Disharmony between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation between Activations