  Data augmentation is commonly used to encode invariances in learning methods.
However, this process is often performed in an inefficient manner, as
artificial examples are created by applying a number of transformations to all
points in the training set. The resulting explosion of the dataset size can be
an issue in terms of storage and training costs, as well as in selecting and
tuning the optimal set of transformations to apply. In this work, we
demonstrate that it is possible to significantly reduce the number of data
points included in data augmentation while realizing the same accuracy and
invariance benefits of augmenting the entire dataset. We propose a novel set of
subsampling policies, based on model influence and loss, that can achieve a 90%
reduction in augmentation set size while maintaining the accuracy gains of
standard data augmentation.
