Abstract
Recently, the generative adversarial networks(GAN) has been widely used in various fields of machine learning. It avoids the complicated solving process of the original generation model while ensuring the generation effect. However, since the inputs of GAN are random initialized, it takes a long time to train the data generated by the model to fit the original data distribution. Therefore, in this paper, we propose a principal component analysis optimized generative adversarial networks (PCA-GAN). The original data is compressed and reduced by principal component analysis to generate the input of the confrontation network, so that the input data retains the characteristics of the original data to some extent, thereby improving the data generation performance and reducing the training time cost. We applied our PCA-GAN to image classification, and the experimental results show that the model effectively improve the accuracy of image classification and enhance the stability of the model.
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Acknowledgements
This work is funded by the National Natural Science Foundation of China under Grant No.61772180, Technological innovation project of Hubei Province 2019(2019AAA047), Green Industry Science and Technology Leadership Program of Hubei University of Technology(No.CPYF2018005) and Hubei province graduate education innovation plan.
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Wang, C., Wu, P., Yan, L. et al. Image classification based on principal component analysis optimized generative adversarial networks. Multimed Tools Appl 80, 9687–9701 (2021). https://doi.org/10.1007/s11042-020-10137-8
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DOI: https://doi.org/10.1007/s11042-020-10137-8