  In practice, data augmentation is assigned a predefined budget in terms of
newly created samples per epoch. When using several types of data augmentation,
the budget is usually uniformly distributed over the set of augmentations but
one can wonder if this budget should not be allocated to each type in a more
efficient way. This paper leverages online learning to allocate on the fly this
budget as part of neural network training. This meta-algorithm can be run at
almost no extra cost as it exploits gradient based signals to determine which
type of data augmentation should be preferred. Experiments suggest that this
strategy can save computation time and thus goes in the way of greener machine
learning practices.
