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Broad Learning Can Tolerate Noise in Image Recognition
Author(s) -
Wang RongLong,
Yu Yang,
Terada Yusuke,
Gao Shangce
Publication year - 2021
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.23280
Subject(s) - mnist database , convolutional neural network , computer science , noise (video) , artificial intelligence , artificial neural network , multilayer perceptron , deep learning , machine learning , perceptron , pattern recognition (psychology) , image (mathematics)
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.