Image Recognition Accuracy, Number of Parameters and Computational Complexity Using Channel Reduction by Dimensional Compression and Attention Function
Image Recognition Accuracy, Number of Parameters and Computational Complexity Using Channel Reduction by Dimensional Compression and Attention Function
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DOI：
10.1145/3592307.3592308
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Zhufeng Li;Hiroyuki Yamauchi
中科院分区：
文献类型：
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作者：
Zhufeng Li;Hiroyuki Yamauchi
目前，由于云服务的发展，人工智能计划正在各个地方使用，并提出了各种机器学习模型。还需要在边缘侧实现AI。但是，在边缘侧，能源供应受到严重限制，因此需要极高的AI。这是研究。当前，随着云服务的发展，在各个地方使用了人工智能计划，并提出了各种机器学习模型。时间问题问题，并且还需要在边缘方面实现AI。但是，由于边缘侧的能源供应的强烈限制，需要具有极高能源效率的AI。本文基于通过se块的间隙压缩的频道降低，并通过sigmoid加权来基于通道注意功能。
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.