EvolveNet: Evolving Networks by Learning Scale of Depth and Width
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...
Main Authors: | Athul Shibu, Dong-Gyu Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-08-01
|
Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/11/16/3611 |
Similar Items
-
Particle Swarm Optimization for Automatically Evolving Convolutional Neural Networks for Image Classification
by: Tom Lawrence, et al.
Published: (2021-01-01) -
Evolving Image Classification Architectures With Enhanced Particle Swarm Optimisation
by: Ben Fielding, et al.
Published: (2018-01-01) -
On the reproducibility of fully convolutional neural networks for modeling time–space-evolving physical systems
by: Wagner G. Pinto, et al.
Published: (2022-01-01) -
Rewarded Meta-Pruning: Meta Learning with Rewards for Channel Pruning
by: Athul Shibu, et al.
Published: (2023-12-01) -
STVG: an evolutionary graph framework for analyzing fast-evolving networks
by: Ikechukwu Maduako, et al.
Published: (2019-06-01)