  Deep convolutional neural networks (CNNs) have achieved remarkable results in
image processing tasks. However, their high expression ability risks
overfitting. Consequently, data augmentation techniques have been proposed to
prevent overfitting while enriching datasets. Recent CNN architectures with
more parameters are rendering traditional data augmentation techniques
insufficient. In this study, we propose a new data augmentation technique
called random image cropping and patching (RICAP) which randomly crops four
images and patches them to create a new training image. Moreover, RICAP mixes
the class labels of the four images, resulting in an advantage similar to label
smoothing. We evaluated RICAP with current state-of-the-art CNNs (e.g., the
shake-shake regularization model) by comparison with competitive data
augmentation techniques such as cutout and mixup. RICAP achieves a new
state-of-the-art test error of $2.19\%$ on CIFAR-10. We also confirmed that
deep CNNs with RICAP achieve better results on classification tasks using
CIFAR-100 and ImageNet and an image-caption retrieval task using Microsoft
COCO.
