人工知能学会第二種研究会資料
Online ISSN : 2436-5556
畳み込みニューラルネットによる学習表現の評価事例ー脳波からの神経科学的知見の復元ー
佐久間 一輝森田 純哉
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研究報告書・技術報告書 オープンアクセス

2020 年 2020 巻 AGI-015 号 p. 07-

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Visualizing deep neural networks (DNN) provides an intuitive explanation for thelearned internal representation, while its evaluation is difficult. We believe that a DNN 's learningrepresentation should be evaluated by its consistency with concepts owned by human. In this study,we represent such a concepts as symbolic binary representations and distributions with variance,and investigated the consistency of a specific neuroscientific concept (P300) with the representationslearned from EEG data obtained in a P300 speller experiment. As a result, we found that theconsistency between the concept and the representation is related to the discrimination accuracyof the DNN.

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