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.
Selectively augmenting difficult to classify points results in efficient training. The authors study the problem of identifying subsampling strategies for data augmentation and propose strategies based on model influence and loss as well as empirical benchmarking of the proposed methods. The authors propose to use influence or loss-based methods to select a subset of points to use in augmenting data sets for training models where the loss is additive over data points.