  Data augmentation is usually adopted to increase the amount of training data,
prevent overfitting and improve the performance of deep models. However, in
practice, random data augmentation, such as random image cropping, is
low-efficiency and might introduce many uncontrolled background noises. In this
paper, we propose Weakly Supervised Data Augmentation Network (WS-DAN) to
explore the potential of data augmentation. Specifically, for each training
image, we first generate attention maps to represent the object's
discriminative parts by weakly supervised learning. Next, we augment the image
guided by these attention maps, including attention cropping and attention
dropping. The proposed WS-DAN improves the classification accuracy in two
folds. In the first stage, images can be seen better since more discriminative
parts' features will be extracted. In the second stage, attention regions
provide accurate location of object, which ensures our model to look at the
object closer and further improve the performance. Comprehensive experiments in
common fine-grained visual classification datasets show that our WS-DAN
surpasses the state-of-the-art methods, which demonstrates its effectiveness.
