A full stage data augmentation method in deep convolutional neural network for natural image classification
type: Item
instance of: scholarly article
title: A full stage data augmentation method in deep convolutional neural network for natural image classification
zbMATH Open document ID: 1496.68292
DOI: 10.1155/2020/4706576
author: Qinghe Zheng
author: Mingqiang Yang
author: Xinyu Tian
author: Nan Jiang
author: De-Qiang Wang
published in: Discrete Dynamics in Nature and Society
publication date: 2020-07-21T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 68T07
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 68T45
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 7223634
zbMATH Keywords: computer vision
zbMATH Keywords: model ensemble
zbMATH Keywords: information transfer
describes a project that uses: DnCNN
describes a project that uses: AlexNet
describes a project that uses: CIFAR
describes a project that uses: ImageNet
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/4706576
OpenAlex ID: W2999209878
Wikidata QID: Q126376445
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description: scientific article; zbMATH DE number 7223634