TeachAugment: Data Augmentation Optimization Using Teacher Knowledge
This addresses the challenge of optimizing data augmentation for improved model generalization in computer vision tasks, representing an incremental improvement over prior adversarial methods.
The paper tackles the problem of adversarial data augmentation causing excessively strong deformations by proposing TeachAugment, which uses a teacher model to generate informative transformed images without careful tuning, resulting in outperforming existing methods in image classification, semantic segmentation, and unsupervised representation learning tasks.
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.