Application of Deep Learning in the Identification and Design of Ancient Ceramics

Abstract

In recent years, deep learning has revolutionized various fields, including pattern recognition and artificial intelligence. Traditional methods for identifying ancient ceramics are often time-consuming and inaccurate. This study explores the feasibility of applying deep learning, particularly Generative Adversarial Networks (GANs), to the identification and design of Ming and Qing dynasty ceramics. GANs, which consist of a generator and a discriminator, can generate realistic images, making them suitable for this application. The research successfully developed innovative color design schemes for antique ceramic products, demonstrating the potential of AI-assisted design to enhance efficiency and creativity in the field. The findings suggest that deep learning can significantly improve the identification and design process of ancient ceramics, providing a new approach to merging traditional art with modern technology.