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Large-scale Image Classification with Multi-perspective Deep Transfer Learning

Bin Wu¹, Tao Zhang² and Li Mao³

1. School of Internet of Things Engineering, Jiangnan University,
   Wuxi, 214122, China
   wubin@jiangnan.edu.cn
2. China Ship Scientific Research Center,
   Wuxi, 214122, China
   taozhang@jiangnan.edu.cn
3. School of Artificial Intelligence and Computer Science, Jiangnan University,
   Wuxi, 214122, China
   wxmaoli@jiangnan.edu.cn

Abstract

Most research efforts on image classification so far have been focused on medium-scale datasets. In addition, there exist other problems, such as difficulty in feature extraction and small sample size. In order to address above difficulties, this paper proposes a multi-perspective convolutional neural network model, which contains channel attention module and spatial attention module. The proposed modules derive attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. In addition, we give the interpretability of the model at multiple scales. Quantitative and qualitative experimental results demonstrate that the accuracy of our proposed model can be improved by up to 3.8% and outperforms the state-of-the-art methods.

Key words

large-scale image classification, channel attention module, spatial attention module, interpretability of the model, multiple scales

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS220714015W

Publication information

Volume 20, Issue 2 (April 2023)
Special Issue on Machine Learning-based Decision Support Systems in IoT systems
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Wu, B., Zhang, T., Mao, L.: Large-scale Image Classification with Multi-perspective Deep Transfer Learning. Computer Science and Information Systems, Vol. 20, No. 2, 743–763. (2023), https://doi.org/10.2298/CSIS220714015W