Knowledge Distillation and Transfer Learning Combined for Innovative Visualization Teaching of Non-Heritage Designs

Abstract

Abstract This study introduces a novel approach by combining knowledge distillation and transfer learning to create a model that, despite its smaller size, approaches the accuracy of its much larger counterparts. It leverages a trained model from a source domain (teacher) to enhance an untrained model in a target domain (student) with significantly fewer parameters. Through transfer learning, we utilize pre-trained deep learning model parameters as initial values. This paper also explores integrating intangible cultural heritage (ICH) information with school curricula, transforming traditional knowledge presentation into intuitive, personalized displays. Our findings highlight that ICH visualization spans nine categories, with traditional arts and crafts leading with 25 items. Interestingly, only 22.58% of students understand Native American culture, pointing towards the potential for educational enhancement. The research suggests designing curriculum with varied teaching activities to improve students’ comprehensive skills.