<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>New social, economic, cultural, and technological trends bring new challenges to designers, and the use of data intelligence analytics is a very promising way to improve the efficiency and quality of their results. In this paper, we use convolutional neural networks to improve and optimize the GAN model, resulting in a more stable DCGAN network model. Visual innovation-related features are inputted into the discriminator in the DCGAN model, and creative visual images are automatically generated in the generator under the constraint label restriction. The network parameters of the DCGAN model are optimized through the training process to construct the brand image design process of automated visual creativity. The performance test analysis of the DCGAN model reveals that the analysis results of its IS index and FID index values are much higher than those of the WGAN and CGAN models, indicating that the generated visual creative images have high quality. The analysis of the visual creativity of the designed brand image found that the subjects’ favorability of the brand image intended by the DCGAN model reached 3.57 points on average and brought a visual feast to the subjects. This paper provides support for the application of Generative Adversarial Networks in the field of brand image design and achieves automated brand creative image design.</p>
</abstract>

New social, economic, cultural, and technological trends bring new challenges to designers, and the use of data intelligence analytics is a very promising way to improve the efficiency and quality of their results. In this paper, we use convolutional neural networks to improve and optimize the GAN model, resulting in a more stable DCGAN network model. Visual innovation-related features are inputted into the discriminator in the DCGAN model, and creative visual images are automatically generated in the generator under the constraint label restriction. The network parameters of the DCGAN model are optimized through the training process to construct the brand image design process of automated visual creativity. The performance test analysis of the DCGAN model reveals that the analysis results of its IS index and FID index values are much higher than those of the WGAN and CGAN models, indicating that the generated visual creative images have high quality. The analysis of the visual creativity of the designed brand image found that the subjects’ favorability of the brand image intended by the DCGAN model reached 3.57 points on average and brought a visual feast to the subjects. This paper provides support for the application of Generative Adversarial Networks in the field of brand image design and achieves automated brand creative image design.

GAN-based Visual Creative Generation Process Automation and Its Application Efficiency in Brand Image Design

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

New social, economic, cultural, and technological trends bring new challenges to designers, and the use of data intelligence analytics is a very promising way...

GAN-based Visual Creative Generation Process Automation and Its Application Efficiency in Brand Image Design

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 20. März 2024

Akzeptiert: 09. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1665

Schlüsselwörter
DCGAN model, Convolutional neural network, Discriminator, Generator, Brand image design

© 2024 Qian Wang., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

GAN-based Visual Creative Generation Process Automation and Its Application Efficiency in Brand Image Design

Online veröffentlicht: 05. Juli 2024

Seitenbereich: -

Eingereicht: 20. März 2024

Akzeptiert: 09. Juni 2024

DOI: https://doi.org/10.2478/amns-2024-1665

SchlüsselwörterDCGAN model, Convolutional neural network, Discriminator, Generator, Brand image design

© 2024 Qian Wang., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
DCGAN model, Convolutional neural network, Discriminator, Generator, Brand image design