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Convergence of multiple deep neural networks for classification with fewer labeled data

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Abstract

With the advent of deep neural networks (DNNs) in the last two decades, tremendous developments have been made in many fields, such as image classification/recognition, voice recognition, and action recognition. These advanced DNNs require large amounts of labeled data, whose collection is costly and requires great effort. In this paper, we provide a convergence method for DNNs to solve some of these difficulties. First, we consider how to create labeled data using a generative adversarial network (GAN), one DNN method, and add additional networks to improve the quality of generated data. Then, we propose a convergence method for the DNNs and use a three-step evaluation to confirm this approach and show how to use the automatically generated data for training. With the method proposed in this paper, we hope that the manual work of labeling data can be reduced for many DNN applications.

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Authors and Affiliations

  1. Department of Computer Information, Hanyang Women‘s University, Seoul, 04763, South Korea

    Chuho Yi

  2. Department of Computer Education, Jeju National University, Jeju, 63243, South Korea

    Jungwon Cho

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  1. Chuho Yi
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  2. Jungwon Cho
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Correspondence to Jungwon Cho.

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Yi, C., Cho, J. Convergence of multiple deep neural networks for classification with fewer labeled data. Pers Ubiquit Comput 27, 1055–1064 (2023). https://doi.org/10.1007/s00779-020-01448-6

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  • DOI: https://doi.org/10.1007/s00779-020-01448-6

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