Data Augmentation Using Random Image Cropping and Patching for Deep CNNs

• Published in: IEEE transactions on circuits and systems for video technology Vol. 30; no. 9; pp. 2917 - 2931
• Main Authors: Takahashi, Ryo, Matsubara, Takashi, Uehara, Kuniaki
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Computer vision; convolutional neural network; Convolutional neural networks; Data augmentation; Image classification; Image color analysis; Image processing; image-caption retrieval; Jitter; Labels; Patching; Principal component analysis; Regularization; Task analysis; Training

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...