Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
It addresses a foundational bottleneck in training deep networks, enabling faster and more stable training across various applications.
The paper tackles the problem of internal covariate shift in deep neural networks, which slows training by requiring lower learning rates and careful initialization, by introducing Batch Normalization to normalize layer inputs; this method achieves the same accuracy with 14 times fewer training steps and improves ImageNet classification to 4.9% top-5 validation error, exceeding human accuracy.
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.