Completely Automated CNN Architecture Design Based on Blocks

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 4; pp. 1242 - 1254
• Main Authors: Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Automatic architecture design; Classification; Competition; Computer applications; Computer architecture; convolutional neural networks (CNNs); Domains; Encoding; Evolutionary computation; evolutionary deep learning; Genetic algorithms; genetic algorithms (GAs); Neural networks; Optimization; Sociology; Statistics; User requirements

The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address...