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Broad Learning Can Tolerate Noise in Image Recognition
IEEJ Transactions on Electrical and Electronic Engineering ( IF 1 ) Pub Date : 2020-10-28 , DOI: 10.1002/tee.23280
Rong‐Long Wang 1 , Yang Yu 2 , Yusuke Terada 3 , Shangce Gao 3
Affiliation  

In recent years, deep learning has achieved very good results because large amounts of learning data have become easily available due to improvements in computer capabilities and big data. However, it has a problem that the accuracy becomes very bad for strong noise. Therefore, in this study, we compare the classification accuracy of existing mainstream neural networks, including broad learning, convolutional neural network and multilayer perceptron. Then, their performance is verified according to the experimental results by using noise‐added MNIST and Fashion MNIST database. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译:

广泛学习可以容忍图像识别中的噪声

近年来,由于计算机功能和大数据的改进,很容易获得大量学习数据,因此深度学习取得了很好的效果。然而,存在这样的问题,即对于强噪声,精度变得非常差。因此,在这项研究中,我们比较了现有主流神经网络的分类准确性,包括广泛学习,卷积神经网络和多层感知器。然后,使用添加了噪声的MNIST和Fashion MNIST数据库根据实验结果验证了它们的性能。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。
更新日期:2020-12-20
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