A Study on Development of Wavelet Deep Learning

Zhong Zhang, Tatsuya Sugino, T. Akiduki, T. Mashimo
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Abstract

In recent years, deep learning that can learn features from a dataset has been remarkably developing in the field of face recognition and voice recognition and so on. However, it is difficult to pursue cause of misjudgment result because input-output relation of deep learning is a black box. Furthermore, the content has yet to be elucidated what the judgment is based on. Therefore, when introducing deep Learning into multiple fields, it is important to understand the reason. This study aims to pursue cause of misjudgment result by intervening in the preprocessing part of deep learning using 2-dimensional discrete wavelet packet transform.
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小波深度学习的发展研究
近年来,能够从数据集中学习特征的深度学习在人脸识别、语音识别等领域得到了显著的发展。然而,由于深度学习的输入输出关系是一个黑盒子,因此很难追究误判结果的原因。此外,内容还有待阐明,判断是基于什么。因此,在将深度学习引入多个领域时,了解其原因非常重要。本研究旨在利用二维离散小波包变换介入深度学习的预处理部分,寻找误判结果的原因。
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