MetaAugment: Sample-Aware Data Augmentation Policy Learning
This work provides a more efficient and effective data augmentation strategy for researchers and practitioners in image recognition, improving model performance by tailoring augmentation policies to individual samples.
This paper addresses the sub-optimality of dataset-level data augmentation policies by proposing a sample-aware approach. They formulate this as a sample reweighting problem, where an augmentation policy network learns to assign weights to augmented images based on a task network's loss on a validation set. This method achieves superior performance on CIFAR-10/100, Omniglot, and ImageNet.
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.