Parametric Deformable Exponential Linear Units for deep neural networks.

Cheng, Qishang; Li, HongLiang; Wu, Qingbo; Ma, Lei; Ngan, King Ngi

Cheng, Qishang; Li, HongLiang; Wu, Qingbo; Ma, Lei; Ngan, King Ngi.

Afiliación

Cheng Q; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China. Electronic address: cqs@std.uestc.edu.cn.
Li H; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China. Electronic address: hlli@uestc.edu.cn.
Wu Q; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China. Electronic address: qbwu@uestc.edu.cn.
Ma L; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China. Electronic address: leima@wit.edu.cn.
Ngan KN; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China. Electronic address: knngan@ee.cuhk.edu.hk.

Neural Netw ; 125: 281-289, 2020 May.

Article en En | MEDLINE | ID: mdl-32151915

Asunto(s)

Aprendizaje Profundo/normas; Bases de Datos Factuales; Reconocimiento de Normas Patrones Automatizadas/métodos

Palabras clave

Deep learning; Deformable exponential; Image classification; Rectified activation

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2020 Tipo del documento: Article Pais de publicación: Estados Unidos

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