标题:A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification
作者:Zheng, Qinghe; Yang, Mingqiang; Tian, Xinyu; Jiang, Nan; Wang, Deqiang
作者机构:[Zheng, Qinghe; Yang, Mingqiang; Wang, Deqiang] Shandong Univ, Sch Informat Sci & Engn, Qingdao 266237, Peoples R China.; [Tian, Xinyu] Shandong Man 更多
通讯作者:Yang, MQ;Wang, DQ
通讯作者地址:[Yang, MQ; Wang, DQ]Shandong Univ, Sch Informat Sci & Engn, Qingdao 266237, Peoples R China.
来源:DISCRETE DYNAMICS IN NATURE AND SOCIETY
出版年:2020
卷:2020
DOI:10.1155/2020/4706576
摘要:Nowadays, deep learning has achieved remarkable results in many computer vision related tasks, among which the support of big data is essential. In this paper, we propose a full stage data augmentation framework to improve the accuracy of deep convolutional neural networks, which can also play the role of implicit model ensemble without introducing additional model training costs. Simultaneous data augmentation during training and testing stages can ensure network optimization and enhance its generalization ability. Augmentation in two stages needs to be consistent to ensure the accurate transfer of specific domain information. Furthermore, this framework is universal for any network architecture and data augmentation strategy and therefore can be applied to a variety of deep learning based tasks. Finally, experimental results about image classification on the coarse-grained dataset CIFAR-10 (93.41%) and fine-grained dataset CIFAR-100 (70.22%) demonstrate the effectiveness of the framework by comparing with state-of-the-art results.
收录类别:SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078185391&doi=10.1155%2f2020%2f4706576&partnerID=40&md5=0904dfee0856b5d3e4d09ded87bdf864
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