  Automated augmentation is an emerging and effective technique to search for
data augmentation policies to improve generalizability of deep neural network
training. Most existing work focuses on constructing a unified policy
applicable to all data samples in a given dataset, without considering sample
or class variations. In this paper, we propose a novel two-stage data
augmentation algorithm, named Label-Aware AutoAugment (LA3), which takes
advantage of the label information, and learns augmentation policies separately
for samples of different labels. LA3 consists of two learning stages, where in
the first stage, individual augmentation methods are evaluated and ranked for
each label via Bayesian Optimization aided by a neural predictor, which allows
us to identify effective augmentation techniques for each label under a low
search cost. And in the second stage, a composite augmentation policy is
constructed out of a selection of effective as well as complementary
augmentations, which produces significant performance boost and can be easily
deployed in typical model training. Extensive experiments demonstrate that LA3
achieves excellent performance matching or surpassing existing methods on
CIFAR-10 and CIFAR-100, and achieves a new state-of-the-art ImageNet accuracy
of 79.97% on ResNet-50 among auto-augmentation methods, while maintaining a low
computational cost.
