Article
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EvolveNet: Evolving Networks by Learning Scale of Depth and Width
Version 1
: Received: 25 July 2023 / Approved: 25 July 2023 / Online: 26 July 2023 (10:19:29 CEST)
A peer-reviewed article of this Preprint also exists.
Shibu, A.; Lee, D.-G. EvolveNet: Evolving Networks by Learning Scale of Depth and Width. Mathematics 2023, 11, 3611. Shibu, A.; Lee, D.-G. EvolveNet: Evolving Networks by Learning Scale of Depth and Width. Mathematics 2023, 11, 3611.
Abstract
Convolutional Neural Networks (CNNs) 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, 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
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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