This study applies deep learning to classify—and interpret—blue-and-white porcelain images by dynastic period and studies the role of multiple object views. A multi-view dataset of Ming and Qing porcelain captured from different angles was curated, resulting in 284 objects and 963 images. Among the evaluated models, a pretrained and fine-tuned ResNet-50 (ImageNet1K-V2) achieved the best performance. Incorporating multiple views of the same object during training improved classification performance compared with a single view. Visual explanation techniques (Grad-CAM, Ablation-CAM, Score-CAM) highlighted shape and motifs as characteristic features. t-SNE visualisation showed learned features cluster by dynasty globally and object locally.
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Edward K.Y. Yapp
Weiming Zhuang
Chen Chen
Social Sciences & Humanities Open
Nanyang Technological University
Singapore University of Social Sciences
Sony (Taiwan)
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Yapp et al. (Tue,) studied this question.
www.synapsesocial.com/papers/69e1cd6f5cdc762e9d856f46 — DOI: https://doi.org/10.1016/j.ssaho.2026.102768