RICAP: Random Image Cropping and Patching Data Augmentation for Deep CNNs


Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara ;
Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:786-798, 2018.


Deep convolutional neural networks (CNNs) have demonstrated remarkable results in image recognition owing to their rich expression ability and numerous parameters. However, an excessive expression ability compared to the variety of training images often has a risk of overfitting. Data augmentation techniques have been proposed to address this problem as they enrich datasets by flipping, cropping, resizing, and color-translating images. They enable deep CNNs to achieve an impressive performance. In this study, we propose a new data augmentation technique called \emph{random image cropping and patching} (\emph{RICAP}), which randomly crops four images and patches them to construct a new training image. Hence, RICAP randomly picks up subsets of original features among the four images and discard others, enriching the variety of training images. Also, RICAP mixes the class labels of the four images and enjoys a benefit similar to label smoothing. We evaluated RICAP with current state-of-the-art CNNs (e.g., shake-shake regularization model) and achieved a new state-of-the-art test error of \textcolor{red}{$2.23%$} on CIFAR-10 among competitive data augmentation techniques such as cutout and mixup. We also confirmed that deep CNNs with RICAP achieved better results on CIFAR-100 and ImageNet than those results obtained by other techniques.

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