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The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer: A Novel Approach in Digital Marketing

The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer: A Novel Approach in Digital Marketing

Hang Zhang, Wenzheng Qu, Huizhen Long, Min Chen
Copyright: © 2024 |Volume: 36 |Issue: 1 |Pages: 26
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9798369324530|DOI: 10.4018/JOEUC.340932
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MLA

Zhang, Hang, et al. "The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer: A Novel Approach in Digital Marketing." JOEUC vol.36, no.1 2024: pp.1-26. http://doi.org/10.4018/JOEUC.340932

APA

Zhang, H., Qu, W., Long, H., & Chen, M. (2024). The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer: A Novel Approach in Digital Marketing. Journal of Organizational and End User Computing (JOEUC), 36(1), 1-26. http://doi.org/10.4018/JOEUC.340932

Chicago

Zhang, Hang, et al. "The Intelligent Advertising Image Generation Using Generative Adversarial Networks and Vision Transformer: A Novel Approach in Digital Marketing," Journal of Organizational and End User Computing (JOEUC) 36, no.1: 1-26. http://doi.org/10.4018/JOEUC.340932

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Abstract

With the continuous evolution of digital marketing, the generation of advertising images has become crucial in capturing user interest and enhancing advertising effectiveness. However, existing methods face limitations in meeting the diverse and creative demands of advertising content, necessitating innovative algorithms to improve advertising generation outcomes. In addressing these challenges, this study proposes a deep learning algorithm framework that cleverly integrates a generative adversarial network and an VGG-based visual transformer model to enhance the effectiveness of advertising image generation. Systematic experimentation shows that the model proposed in this article achieves an AUC metric value of more than 0.7 on several datasets. The results of the experiments demonstrate that the novel algorithm significantly improves the attractiveness of advertising content, particularly showcasing substantial benefits in website operations during online evaluation experiments.