A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification

Qinghe Zheng, Mingqiang Yang, Xinyu Tian, Nan Jiang and Deqiang Wang

Discrete Dynamics in Nature and Society, 2020, vol. 2020, 1-11

Abstract:

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:4706576

DOI: 10.1155/2020/4706576

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