  Optimization of image transformation functions for the purpose of data
augmentation has been intensively studied. In particular, adversarial data
augmentation strategies, which search augmentation maximizing task loss, show
significant improvement in the model generalization for many tasks. However,
the existing methods require careful parameter tuning to avoid excessively
strong deformations that take away image features critical for acquiring
generalization. In this paper, we propose a data augmentation optimization
method based on the adversarial strategy called TeachAugment, which can produce
informative transformed images to the model without requiring careful tuning by
leveraging a teacher model. Specifically, the augmentation is searched so that
augmented images are adversarial for the target model and recognizable for the
teacher model. We also propose data augmentation using neural networks, which
simplifies the search space design and allows for updating of the data
augmentation using the gradient method. We show that TeachAugment outperforms
existing methods in experiments of image classification, semantic segmentation,
and unsupervised representation learning tasks.
