EvolveNet: Evolving Networks by Learning Scale of Depth and Width

Athul Shibu and Dong-Gyu Lee ()
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Athul Shibu: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea
Dong-Gyu Lee: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea

Mathematics, 2023, vol. 11, issue 16, 1-14

Abstract: Convolutional neural networks (CNNs) have shown decent performance in a variety of computer vision tasks. However, these network configurations are largely hand-crafted, which leads to inefficiency in the constructed network. Various other algorithms have been proposed to address this issue, but the inefficiencies resulting from human intervention have not been addressed. Our proposed EvolveNet algorithm is a task-agnostic evolutionary search algorithm that can find optimal depth and width scales automatically in an efficient way. The optimal configurations are not found using grid search, and are instead evolved from an existing network. This eliminates inefficiencies that emanate from hand-crafting, thus reducing the drop in accuracy. The proposed algorithm is a framework to search through a large search space of subnetworks until a suitable configuration is found. Extensive experiments on the ImageNet dataset demonstrate the superiority of the proposed method by outperforming the state-of-the-art methods.

Keywords: convolutional neural network; network scaling; evolutionary computation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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