The Generation and Analysis of Art Image Based on Generative Adversarial Network

Jingfeng Li

2023

Abstract

Recent years have witnessed a significant surge in the attention garnered by Generative Adversarial Networks (GAN), owing to their remarkable capability to generate high-quality and realistic images. The objective of this research is to devise a model based on GAN that can effectively produce images with diverse and realistic attributes, ensuring a high level of quality. The proposed method involves training a GAN architecture consisting of a generator and a discriminator. In the training process, GAN engage in an adversarial game between the generator and discriminator models. The generator and discriminator of a GAN use a deep convolutional neural network (CNN) architecture to continuously improve performance. The generated images are efficiently transformed by a series of deconvolutional layers in the generator to incorporate random noise inputs. This study selects the dataset which include various artists about portraits. The proposed GAN model has been demonstrated to successfully generate high-quality images, as supported by the experimental results. The generated images exhibit diverse features and demonstrate the effectiveness of the GAN architecture in picking up the patterns in the portraits. In conclusion, this research highlights the potential of GANs in image generation.

Paper Citation

in Harvard Style

Li J. (2023). The Generation and Analysis of Art Image Based on Generative Adversarial Network. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 224-228. DOI: 10.5220/0012799200003885

in Bibtex Style

@conference{daml23,
author={Jingfeng Li},
title={The Generation and Analysis of Art Image Based on Generative Adversarial Network},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={224-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012799200003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - The Generation and Analysis of Art Image Based on Generative Adversarial Network
SN - 978-989-758-705-4
AU - Li J.
PY - 2023
SP - 224
EP - 228
DO - 10.5220/0012799200003885
PB - SciTePress