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Active weighted mapping-based residual convolutional neural network for image classification

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A Correction to this article was published on 07 October 2021

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Abstract

In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using identical mapping by a shortcut connection. It results in multiple paths of data flow under a network and the paths are merged with the equal weights. However, it is questionable whether it is correct to use the fixed and predefined weights at the mapping units of all paths. In this paper, we introduce the active weighted mapping method which infers proper weight values based on the characteristic of input data on the fly. The weight values of each mapping unit are not fixed but changed as the input image is changed, and the most proper weight values for each mapping unit are derived according to the input image. For this purpose, channel-wise information is embedded from both the shortcut connection and convolutional block, and then the fully connected layers are used to estimate the weight values for the mapping units. We train the backbone network and the proposed module alternately for a more stable learning of the proposed method. Results of the extensive experiments show that the proposed method works successfully on the various backbone architectures from ResNet to DenseNet. We also verify the superiority and generality of the proposed method on various datasets in comparison with the baseline.

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Acknowledgements

This work was supported by a Research and Development project, Enabling a System for Sharing and Disseminating Research Data of Korea Institute of Science and Technology (KISTI), South Korea, under Grant K-20-L01-C04-S01. The corresponding author is Dr. Wonjun Hwang.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Ajou University, San 5-1, Woncheon-dong, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Korea

    Hyoungho Jung & Wonjun Hwang

  2. Research Data Sharing Center, Korea Institute of Science and Technology Information, Daejeon, 34141, South Korea

    Ryong Lee & Sang-Hwan Lee

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  1. Hyoungho Jung
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  2. Ryong Lee
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  3. Sang-Hwan Lee
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  4. Wonjun Hwang
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Correspondence to Wonjun Hwang.

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The original online version of this article was revised: The given name of the first author was misspelled as “Hyungho” and the images of Figs. 7, 8, 9and 10 were interchanged.

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Jung, H., Lee, R., Lee, SH. et al. Active weighted mapping-based residual convolutional neural network for image classification. Multimed Tools Appl 80, 33139–33153 (2021). https://doi.org/10.1007/s11042-020-09808-3

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  • DOI: https://doi.org/10.1007/s11042-020-09808-3

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