2024
DOI: 10.12720/jait.15.5.565-571
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A Speed-up Channel Attention Technique for Accelerating the Learning Curve of a Binarized Squeeze-and-Excitation (SE) Based ResNet Model

Wu Shaoqing,
Hiroyuki Yamauchi

Abstract: The use of 1-bit representation for network weights, as opposed to the conventional 32-bit, has been investigated to save on the required power and memory footprint. Squeeze-and-Excitation (SE) based channel attention techniques aim to further reduce the number of parameters by eliminating redundant channels. However, this approach leads to a significant drawback of an unstable and slow learning curve, especially when compared to fitting parameters in SE networks. To address this issue, this paper presents the… Show more

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