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Separable Attention Capsule Network for Signal Classification
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DOI：10.1109/ACCESS.2020.3027855.png)
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Abstract
En 中文
In this paper, a new Separable Attention Capsule Network (SACN) is proposed for signal classification. SACN is a light-weight network composed of multi-channel separable convolution layer, attention module and classification layer. First, depth-wise convolution is employed to extract features of signals in a low-complexity manner, and the multi-channel network structure is designed to increase the network width to improve the diversity of features of signals. Then a channel attention module is followed by a capsule network whose element contains a group of neurons. This attention module can explore the interdependence among channels to use global information to selectively strengthen some important channels, thus achieving the improvement of generalization ability of SACN. Some experiments are taken on several datasets with communication and radar signals, and the comparison results prove the efficiency of SACN and the superiority to its counterparts.
Keywords:
Convolution
Feature extraction
Kernel
Pattern classification
Radar
Time-frequency analysis
Machine learning
Separable convolution
multi-channel
capsule network
channel attention
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