  Augmenting training datasets has been shown to improve the learning
effectiveness for several computer vision tasks. A good augmentation produces
an augmented dataset that adds variability while retaining the statistical
properties of the original dataset. Some techniques, such as AutoAugment and
Fast AutoAugment, have introduced a search phase to find a set of suitable
augmentation policies for a given model and dataset. This comes at the cost of
great computational overhead, adding up to several thousand GPU hours. More
recently RandAugment was proposed to substantially speedup the search phase by
approximating the search space by a couple of hyperparameters, but still
incurring non-negligible cost for tuning those. In this paper we show that,
under the assumption that the augmentation space is approximately distribution
invariant, a uniform sampling over the continuous space of augmentation
transformations is sufficient to train highly effective models. Based on that
result we propose UniformAugment, an automated data augmentation approach that
completely avoids a search phase. In addition to discussing the theoretical
underpinning supporting our approach, we also use the standard datasets, as
well as established models for image classification, to show that
UniformAugment's effectiveness is comparable to the aforementioned methods,
while still being highly efficient by virtue of not requiring any search.
