The Role of Transformer-cGANs Fusion in Digital Marketing

Advertising images play a key role in digital marketing and are an indispensable tool to attract users, convey messages and promote products. However, current advertising image generation faces many challenges, including insufficient image quality and diversity, and high production costs. In this context, the rise of deep learning technology provides a powerful tool to solve these problems. Deep learning is increasingly used in the field of computer vision, bringing new hope for advertising image generation. This article introduces an innovative Transformer-cGANs model that combines the sequence modeling capabilities of Transformer with the image generation technology of Generative Adversarial Networks (GANs). This model shows excellent performance in advertising image generation tasks in digital marketing. Through extensive experimental verification, our model successfully overcomes the insufficient image quality and diversity issues present in traditional methods.

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Publication Details

Journal
Journal of Organizational and End User Computing
Published
2024-08-12
DOI
https://doi.org/10.4018/joeuc.347914
Citations
1
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.25
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article

The Role of Transformer-cGANs Fusion in Digital Marketing

Chunlai Song
1 citations
Journal of Organizational and End User Computing
Generative Adversarial Networks and Image Synthesis
0.25
article

The Role of Transformer-cGANs Fusion in Digital Marketing

Chunlai Song
article en
1 citations

Abstract

Advertising images play a key role in digital marketing and are an indispensable tool to attract users, convey messages and promote products. However, current advertising image generation faces many challenges, including insufficient image quality and diversity, and high production costs. In this context, the rise of deep learning technology provides a powerful tool to solve these problems. Deep learning is increasingly used in the field of computer vision, bringing new hope for advertising image generation. This article introduces an innovative Transformer-cGANs model that combines the sequence modeling capabilities of Transformer with the image generation technology of Generative Adversarial Networks (GANs). This model shows excellent performance in advertising image generation tasks in digital marketing. Through extensive experimental verification, our model successfully overcomes the insufficient image quality and diversity issues present in traditional methods.

Journal of Organizational and End User ComputingVol. 36(1)
Kyungil University (KR)
Industry, innovation and infrastructure
Openalex Percentile: Top 49%
Generative Adversarial Networks and Image Synthesis
0.25
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