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This research evaluates the effectiveness of GANs in generating realistic AI-driven artwork.

Fusion of art design and technology in contemporary design

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Randomized trial evaluates AI art generation quality using GANs, suggesting significant advancements in digital art.

This research evaluates the effectiveness of GANs in generating realistic AI-driven artwork.
Utilized convolutional neural networks within generative adversarial networks to enhance art quality.
Compared performance metrics such as recall, accuracy, precision, and F1-score against existing models like CycleGAN, Pix2Pix, and StyleGAN.
Conducted performance evaluation on classification accuracy, feature similarity, and perceptual quality metrics.
The proposed GAN model significantly outperformed CycleGAN, Pix2Pix, and StyleGAN in generating visually appealing art.
Demonstrated enhanced generalization and classification accuracy compared to traditional techniques.
AI-generated artwork increasingly resembles traditional art, indicating a shift toward acceptance in the art community.

Bofen Su (2026) studied this question.

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