Article: Style transfer method for ceramic patterns in the Liao, Jin, and Yuan dynasties based on VGG16 structure and CycleGAN algorithm Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.41 pp.45 - 69 Abstract: Traditional methods face challenges such as weak feature extraction, content distortion, and unstable training in the style transfer of Liao, Jin, and Yuan ceramic patterns. This study proposes a style transfer model based on an improved VGG16 and a cyclic consistency adversarial network, integrating multi-channel input, spatial attention, and a multi-scale feature pyramid, with a three-scale discriminator and structural similarity constraints. Experiments show that in Liao-to-Jin transfer, style accuracy reached 89.7%, content retention 91.2%, SSIM 0.801, and PSNR 27.9 dB. In Jin-to-Yuan and Yuan-to-Liao transfers, average accuracy and retention were 88.4% and 90.5%, with SSIM 0.792 and PSNR 27.5 dB. The three-scale discriminator improved style fidelity by 6.1%, and expert ratings averaged 8.7-9.1. While the method preserves semantic content and enhances stability for flat painted patterns, performance decreases for engraved designs with strong 3D features (e.g., Yaozhou kiln), with a success rate of 78.3%. Inderscience Publishers - linking academia, business and industry through research

Title: Style transfer method for ceramic patterns in the Liao, Jin, and Yuan dynasties based on VGG16 structure and CycleGAN algorithm

Authors: Congmei Zhao; Xin Wang

Addresses: School of Design, Inner Mongolia Normal University, Hohhot, 010000, China ' School of Journalism and Communication, Inner Mongolia Normal Universit Hohhot, 010000, China

Abstract: Traditional methods face challenges such as weak feature extraction, content distortion, and unstable training in the style transfer of Liao, Jin, and Yuan ceramic patterns. This study proposes a style transfer model based on an improved VGG16 and a cyclic consistency adversarial network, integrating multi-channel input, spatial attention, and a multi-scale feature pyramid, with a three-scale discriminator and structural similarity constraints. Experiments show that in Liao-to-Jin transfer, style accuracy reached 89.7%, content retention 91.2%, SSIM 0.801, and PSNR 27.9 dB. In Jin-to-Yuan and Yuan-to-Liao transfers, average accuracy and retention were 88.4% and 90.5%, with SSIM 0.792 and PSNR 27.5 dB. The three-scale discriminator improved style fidelity by 6.1%, and expert ratings averaged 8.7-9.1. While the method preserves semantic content and enhances stability for flat painted patterns, performance decreases for engraved designs with strong 3D features (e.g., Yaozhou kiln), with a success rate of 78.3%.

Keywords: ceramic decoration; Liao, Jin, Yuan; style transfer; VGG16; CycleGAN.

DOI: 10.1504/IJICT.2026.153376

International Journal of Information and Communication Technology, 2026 Vol.27 No.41, pp.45 - 69

Received: 11 Oct 2025
Accepted: 05 Dec 2025
Published online: 06 May 2026 *

Title: Style transfer method for ceramic patterns in the Liao, Jin, and Yuan dynasties based on VGG16 structure and CycleGAN algorithm

Keep up-to-date