Design of an art style transfer teaching platform based on generative adversarial networks
Henan Zeng, Yu Shu · International Journal of Arts and Technology · 2026
This paper proposed generative adversarial network (GAN) with a shared latent space (sLS-GAN) to improve controllability and cultural adaptability in art style transfer.By integrating a variational autoencoder (VAE) with adversarial learning, it constructs a shared latent space that enables high-quality bidirectional translation between greyscale and colour image domains.Latent-space alignment improves realism and semantic coherence, while cycle-consistency regularises forward-backward mappings.A dual up-sampling/dual down-sampling design enhances structural stability across domains.In addition, a residual saliency network strengthens salient-region modelling and improves efficiency, reducing reliance on explicit content-preservation constraints.Experiments on the WikiArt Paintings and SemArt datasets show that sLS-GAN achieves an FID of 106.45 on WikiArt and outperforms representative baselines in Inception Score and PSNR, indicating improved semantic consistency, diversity, and perceptual quality.In greyscale colorisation, sLS-GAN reduces parameters by 89.5% and FLOPs by 87.8% versus conventional models, delivering substantial computational savings.