  Large-batch SGD is important for scaling training of deep neural networks.
However, without fine-tuning hyperparameter schedules, the generalization of
the model may be hampered. We propose to use batch augmentation: replicating
instances of samples within the same batch with different data augmentations.
Batch augmentation acts as a regularizer and an accelerator, increasing both
generalization and performance scaling. We analyze the effect of batch
augmentation on gradient variance and show that it empirically improves
convergence for a wide variety of deep neural networks and datasets. Our
results show that batch augmentation reduces the number of necessary SGD
updates to achieve the same accuracy as the state-of-the-art. Overall, this
simple yet effective method enables faster training and better generalization
by allowing more computational resources to be used concurrently.
