  Batch Normalization (BN) is capable of accelerating the training of deep
models by centering and scaling activations within mini-batches. In this work,
we propose Decorrelated Batch Normalization (DBN), which not just centers and
scales activations but whitens them. We explore multiple whitening techniques,
and find that PCA whitening causes a problem we call stochastic axis swapping,
which is detrimental to learning. We show that ZCA whitening does not suffer
from this problem, permitting successful learning. DBN retains the desirable
qualities of BN and further improves BN's optimization efficiency and
generalization ability. We design comprehensive experiments to show that DBN
can improve the performance of BN on multilayer perceptrons and convolutional
neural networks. Furthermore, we consistently improve the accuracy of residual
networks on CIFAR-10, CIFAR-100, and ImageNet.
