Analysis of the mechanism of generative adversarial networks learning based on gray image

Chaozhi Geng

doi:10.1117/12.2655967

12 January 2023 Analysis of the mechanism of generative adversarial networks learning based on gray image

Chaozhi Geng

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125091R (2023) https://doi.org/10.1117/12.2655967
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Machine learning has a crucial role in people's lives. Machine learning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help people with image recognition, stock prediction, etc. In addition, machine learning has a subset called deep learning (DL). DL in machine learning can help people optimize and learn models better. DL in machine learning can help people optimize and learn models better. One of them is generative adversarial networks (GANs). The other one is Convolutional Neural Networks (CNNs). First, GANs have achieved impressive results in image generation, data enhancement, etc. However, effective intervention and control of the results of generative adversarial networks is a challenging problem. Based on grayscale image data, this paper investigates the change patterns of the generation results of generative adversarial networks in the face of different input data. There are three groups in the experiment. The first group is to explore the effect of the size of the dataset on the generated images. The second group is to explore the effect of the diversity of the book dataset on the results. The third group was to explore the effect of the overall dataset on the generative images. The experimentally generated images revealed that data size and diversity are important factors that affect the quality of the results generated by generative adversarial networks. Among other things, the increase in the amount of data does not always positively affect the generative results.

Citation Download Citation

Chaozhi Geng "Analysis of the mechanism of generative adversarial networks learning based on gray image", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125091R (12 January 2023); https://doi.org/10.1117/12.2655967

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KEYWORDS

Machine learning

Neural networks

Convolution

Convolutional neural networks

Image classification

Image filtering

Performance modeling

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