Image Recognition Accuracy, Number of Parameters and Computational Complexity Using Channel Reduction by Dimensional Compression and Attention Function
Pages 1 - 7
Abstract
Currently, due to the development of cloud services, artificial intelligence programs are being used in various places, and various machine learning models have been proposed. AI is also required to be realized on the edge side. However, on the edge side, the energy supply is severely limited, so an extremely energy-efficient AI is required. It is research. Currently, with the development of cloud services, artificial intelligence programs are used in various places, and various machine learning models have been proposed. There is a problem of time, and AI is required to be realized on the edge side as well. However, AI with extremely high energy efficiency is required due to the strong limitation of energy supply on the edge side. This paper is based on channel reduction by dimensional compression by GAP of SE blocks and channel attention function by weighting by sigmoid.
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Index Terms
- Image Recognition Accuracy, Number of Parameters and Computational Complexity Using Channel Reduction by Dimensional Compression and Attention Function
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Published In
March 2023
316 pages
ISBN:9798400700002
DOI:10.1145/3592307
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Association for Computing Machinery
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Published: 14 August 2023
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ICECC 2023
ICECC 2023: 2023 The 6th International Conference on Electronics, Communications and Control Engineering
March 24 - 26, 2023
Fukuoka, Japan
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