Generation of high-quality image using generative adversarial network

Yitao Sun

doi:10.1117/12.2639479

17 May 2022 Generation of high-quality image using generative adversarial network

Yitao Sun

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 122595Z (2022) https://doi.org/10.1117/12.2639479
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

Deep learning enables a computer to construct complex concepts from simpler ones by learning data representation with abstraction. However, deep learning requires a large number of samples for training to prevent over-fitting. Generative adversarial network (GAN) is a kind of unsupervised learning that can be used to synthesize images. Generating higher quality images using GAN is conducive to improving the training efficiency of deep learning. How to generate high quality and high-diversity samples using GAN to expand the sample size for deep learning training is quite appealing. In this paper, the concept of generative adversarial network including network structure and four popular loss functions is introduced. Furthermore, three typical GAN-based models to improve the image quality or diversity have been presented. Finally, the paper discusses the shortcomings of GANs to steer future research in the right direction. Generation of high quality images using GAN has great potential to help deep learning models to reduce the over-fitting problem in the near future.

Citation Download Citation

Yitao Sun "Generation of high-quality image using generative adversarial network", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 122595Z (17 May 2022); https://doi.org/10.1117/12.2639479

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KEYWORDS

Gallium nitride

Artificial intelligence

Data modeling

Image quality

Image processing

Machine learning

Neural networks

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