# Can Deep Learning Identify Early Chinese Ceramics Using Only 2D Images?

## TL;DR

This paper explores whether deep learning can identify early Chinese ceramics using only 2D images, showing promising results for visual feature recognition and dating.

## Contribution

The study introduces deep learning for large-scale recognition of early Chinese ceramics using 2D images, addressing class imbalance in multi-label tasks.

## Key findings

- Deep learning can accurately recognize visual features like glaze and shape types in early Chinese ceramics.
- Ceramic dating is feasible for major dynasties but remains challenging across the entire historical timeline.
- Cultural continuity can lead to false dating by classifying ceramics into adjacent dynasties.

## Abstract

Study of early Chinese ceramics is crucial for understanding cultural, economic, and technological developments in Chinese history. With the evolving deep learning techniques, one urgent question would be, whether we can identify early Chinese ceramics by a simple 2D image without further domain knowledge. This work collected a highly diverse dataset for ancient Chinese ceramics from 15 dynasties, with 4 representative glaze colors and 15 shape types. We studied the performance of five state-of-the-art neural networks on two identification tasks: ceramic visual feature recognition and early Chinese ceramic dating. A class-imbalance learning strategy is designed to improve the models’ performance on multi-label tasks. To the best of our knowledge, our work is the first to introduce deep learning into early Chinese ceramic recognition on a large scale. Experiments prove that deep learning can recognize visual features like glaze and most shape types with high accuracy, while ceramic dating is feasible for the main dynasties but remains challenging along the overall history. Further quantitative assessment shows that cultural inheritance and artistic continuity can lead to reasonable false dating by classifying ceramics into adjacent dynasties or periods. Moreover, although domain knowledge is required for interpretation, deep learning shows great potential in recognizing even unlabeled time-relevant features, which can help study the inheritance and evolution of early Chinese ceramic development.