  Automated data augmentation has shown superior performance in image
recognition. Existing works search for dataset-level augmentation policies
without considering individual sample variations, which are likely to be
sub-optimal. On the other hand, learning different policies for different
samples naively could greatly increase the computing cost. In this paper, we
learn a sample-aware data augmentation policy efficiently by formulating it as
a sample reweighting problem. Specifically, an augmentation policy network
takes a transformation and the corresponding augmented image as inputs, and
outputs a weight to adjust the augmented image loss computed by a task network.
At training stage, the task network minimizes the weighted losses of augmented
training images, while the policy network minimizes the loss of the task
network on a validation set via meta-learning. We theoretically prove the
convergence of the training procedure and further derive the exact convergence
rate. Superior performance is achieved on widely-used benchmarks including
CIFAR-10/100, Omniglot, and ImageNet.
