Achieving an optimal group structure in a neural architecture search

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초록

The method proposed in this letter searches for an effective group structure of group convolutions in a convolutional neural network that can improve the classification accuracy. The model's group structure is obtained using an effective differential neural architecture search. Our code can be accessed at https://github.com/minercode625/grunas.git. © 2022 The Authors. Electronics Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

제목
Achieving an optimal group structure in a neural architecture search
저자
Seo, W.; Park, J.; Lim, W.; Kim, Dae-Won; Lee, Jaesung
DOI
10.1049/ell2.12585
발행일
2022-09
유형
Article
저널명
Electronics Letters
권
58
호
19
페이지
732 ~ 733

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