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<p>Combined with the creation process of AI painting art, it analyzes the artistic design characteristics of AI paintings formed by generative adversarial networks. It utilizes a convolutional neural network to extract the artistic characteristics of AI paintings and combines the error of feature loss to calculate the features, which ensures the stable operation of the generative adversarial network model. To achieve the style migration of AI painting artworks, the Cycle GAN model was designed on this basis. Comparing the features of both AI paintings of generative adversarial networks and paintings of human artists, the perceptual complexity is taken as the dependent variable, and a regression model is established to analyze and calculate the complexity features of AI paintings, as well as to analyze the color matching art of AI paintings by combining the beauty calculation method. According to the comparison results, the AI paintings have a score of 3.71 for inspirational, 3.69 for aesthetic value, 3.52 for compositional rationality, and 3.38 for breakthrough. The AI paintings have a high level of thought and inspirational value.</p>
</abstract>

Combined with the creation process of AI painting art, it analyzes the artistic design characteristics of AI paintings formed by generative adversarial networks. It utilizes a convolutional neural network to extract the artistic characteristics of AI paintings and combines the error of feature loss to calculate the features, which ensures the stable operation of the generative adversarial network model. To achieve the style migration of AI painting artworks, the Cycle GAN model was designed on this basis. Comparing the features of both AI paintings of generative adversarial networks and paintings of human artists, the perceptual complexity is taken as the dependent variable, and a regression model is established to analyze and calculate the complexity features of AI paintings, as well as to analyze the color matching art of AI paintings by combining the beauty calculation method. According to the comparison results, the AI paintings have a score of 3.71 for inspirational, 3.69 for aesthetic value, 3.52 for compositional rationality, and 3.38 for breakthrough. The AI paintings have a high level of thought and inspirational value.

Artistic characterization of AI painting based on generative adversarial networks

Applied Mathematics and Nonlinear Sciences

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

Combined with the creation process of AI painting art, it analyzes the artistic design characteristics of AI paintings formed by generative adversarial...

Artistic characterization of AI painting based on generative adversarial networks

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 26. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0238

Schlüsselwörter
Generative adversarial network, Convolutional neural network, Regression model, AI painting

© 2024 Weiwei Lu et al., published by Sciendo

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

Artistic characterization of AI painting based on generative adversarial networks

Online veröffentlicht: 31. Jan. 2024

Seitenbereich: -

Eingereicht: 20. Dez. 2023

Akzeptiert: 26. Dez. 2023

DOI: https://doi.org/10.2478/amns-2024-0238

SchlüsselwörterGenerative adversarial network, Convolutional neural network, Regression model, AI painting

© 2024 Weiwei Lu et al., published by Sciendo

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

Schlüsselwörter
Generative adversarial network, Convolutional neural network, Regression model, AI painting