Cargando…
Multiscale Hybrid Convolutional Deep Neural Networks with Channel Attention
Attention mechanisms can improve the performance of neural networks, but the recent attention networks bring a greater computational overhead while improving network performance. How to maintain model performance while reducing complexity is a hot research topic. In this paper, a lightweight Mixture...
Autores principales: | Yang, Hua, Yang, Ming, He, Bitao, Qin, Tao, Yang, Jing |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497715/ https://www.ncbi.nlm.nih.gov/pubmed/36141066 http://dx.doi.org/10.3390/e24091180 |
Ejemplares similares
-
Multiscale Convolutional Neural Networks with Attention for Plant Species Recognition
por: Wang, Xianfeng, et al.
Publicado: (2021) -
Multiscale Convolutional Neural Network Based on Channel Space Attention for Gearbox Compound Fault Diagnosis
por: Xu, Qinghong, et al.
Publicado: (2023) -
Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network
por: Wang, Bo, et al.
Publicado: (2022) -
Convolutional Neural Network with Multiscale Fusion and Attention Mechanism for Skin Diseases Assisted Diagnosis
por: Li, Zhong, et al.
Publicado: (2022) -
Hybrid convolution neural network with channel attention mechanism for sensor-based human activity recognition
por: Mekruksavanich, Sakorn, et al.
Publicado: (2023)