The scarcity of training data presents a fundamental challenge in applying deep learning to archaeological artifact classification, particularly for the rare types of Chinese porcelain. This study investigates whether synthetic images generated through Stable Diffusion with Low-Rank Adaptation (LoRA) can effectively augment limited real datasets for multi-task CNN-based porcelain classification. Using MobileNetV3 with transfer learning, we conducted controlled experiments comparing models trained on pure real data against those trained on mixed real-synthetic datasets (95:5 and 90:10 ratios) across four classification tasks: dynasty, glaze, kiln and type identification. Results demonstrate task-specific benefits: type classification showed the most substantial improvement (5.5\% F1-macro increase with 90:10 ratio), while dynasty and kiln tasks exhibited modest gains (3-4\%), suggesting that synthetic augmentation effectiveness depends on the alignment between generated features and task-relevant visual signatures. Our work contributes practical guidelines for deploying generative AI in archaeological research, demonstrating both the potential and limitations of synthetic data when archaeological authenticity must be balanced with data diversity.

翻译：训练数据的稀缺性是深度学习应用于考古文物分类的根本挑战，尤其对于稀有类型的中国瓷器而言。本研究探讨了通过低秩自适应（LoRA）的稳定扩散技术生成的合成图像，能否有效增强基于CNN的多任务瓷器分类中有限的真实数据集。采用迁移学习的MobileNetV3架构，我们通过控制实验对比了纯真实数据训练模型与混合真实-合成数据集（95:5和90:10比例）训练模型在四项分类任务中的表现：朝代、釉色、窑口和器型鉴定。结果显示任务特异性增益：器型分类提升最为显著（90:10比例下F1-macro指标提高5.5%），朝代与窑口任务则呈现适度改善（3-4%），这表明合成增强的有效性取决于生成特征与任务相关视觉特征之间的匹配程度。本研究为生成式人工智能在考古研究中的部署提供了实用指南，揭示了在考古真实性与数据多样性必须取得平衡时，合成数据既具潜力又存在局限性的双重特性。