Re-Thinking the Effectiveness of Batch Normalization and Beyond.

Batch normalization (BN) is used by default in many modern deep neural networks due to its effectiveness in accelerating training convergence and boosting inference performance. Recent studies suggest that the effectiveness of BN is due to the Lipschitzness of the loss and gradient, rather than the reduction of internal covariate shift. However, questions remain about whether Lipschitzness is sufficient to explain the effectiveness of BN and whether there is room for vanilla BN to be further improved. To answer these questions, we first prove that when stochastic gradient descent (SGD) is applied to optimize a general non-convex problem, three effects will help convergence to be faster and better: (i) reduction of the gradient Lipschitz constant, (ii) reduction of the expectation of the square of the stochastic gradient, and (iii) reduction of the variance of the stochastic gradient. We demonstrate that vanilla BN only with ReLU can induce the three effects above, rather than Lipschitzness, but vanilla BN with other nonlinearities like Sigmoid, Tanh, and SELU will result in degraded convergence performance. To improve vanilla BN, we propose a new normalization approach, dubbed complete batch normalization (CBN), which changes the placement position of normalization and modifies the structure of vanilla BN based on the theory. It is proven that CBN can elicit all the three effects above, regardless of the nonlinear activation used. Extensive experiments on benchmark datasets CIFAR10, CIFAR100, and ILSVRC2012 validate that CBN makes the training convergence faster, and the training loss converges to a smaller local minimum than vanilla BN. Moreover, CBN helps networks with multiple nonlinear activations (Sigmoid, Tanh, ReLU, SELU, and Swish) achieve higher test accuracy steadily. Specifically, benefitting from CBN, the classification accuracies for networks with Sigmoid, Tanh, and SELU are boosted by more than 15.0%, 4.5%, and 4.0% on average, respectively, which is even comparable to the performance for ReLU.