转译与重构——生成对抗网络在传统剪纸中的创新路径

Translation and Reconstruction:Innovative Pathways of GenerativeAdversarial Networks in Traditional Paper-cutting Art

生成对抗网络(GANs)作为一种深度学习领域的突破性技术,通过生成器与判别器的对抗性训练机制,实现了对数据集分布的精确模拟和高质量样本的生成。聚焦于GANs在传统剪纸艺术中的创新应用,揭示了在提升创作效率、降低成本以及实现个性化定制方面的巨大潜力。传统剪纸艺术面临机械化生产和文化认同感削弱的挑战时,GANs技术的应用不仅拓宽了传统艺术表现的多样性,还增强了视觉吸引力和用户体验,为传统艺术的现代化转型提供了新路径。通过深度学习剪纸艺术特征,GANs技术能够精准模拟并创新设计传统图案,为非物质文化遗产数字化保护与传承提供技术支撑。通过对数据集构建、模型训练、结果评估、文化适应性及二次创作等方面进行系统分析,并探讨其模型在剪纸艺术生成过程中的文化适应性和真实性等问题。随着技术的不断发展,GANs技术有望在设计更广泛的领域内发挥其创新潜力。

As a breakthrough technology in the field of deep learning,Generative Adversarial Network(GANs)enables precise simulation of dataset distributions and the generation of high-quality samples through an adversarial training mechanism involving generators and discriminators.This paper focuses on the innovative application of GANs in the traditional paper-cutting art,unveiling their significant potential to enhance creative efficiency,reduce costs,and facilitate personalized customization.As the traditional paper-cutting art faces challenges from mechanized production and diminishing cultural identity,the application of GANs not only expands the diversity of traditional artistic expressions but also enhances visual appeal and user experience,offering a new pathway for the modern transformation of traditional arts.By deep learning the characteristics of the paper-cutting art,GANs can accurately simulate and innovatively design traditional patterns,providing technical support for the digital preservation and inheritance of intangible cultural heritage.This paper systematically analyzes dataset construction,model training,result evaluation,cultural adaptability,and secondary creation,and explores issues such as the cultural adaptability and authenticity of GAN models in the generative process of the paper-cutting art.With the continuous development of technology,GANs are poised to unleash their innovative potential in a broader range of design fields.