Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm. This work introduces Style Filter, a tailored methodology for industrial contexts. By selectively filtering source domain data before knowledge transfer, Style Filter reduces the quantity of data while maintaining or even enhancing the performance of transfer learning strategy. Offering label-free operation, minimal reliance on prior knowledge, independence from specific models, and re-utilization, Style Filter is evaluated on authentic industrial datasets, highlighting its effectiveness when employed before conventional transfer strategies in the deep learning domain. The results underscore the effectiveness of Style Filter in real-world industrial applications.

翻译：针对工业领域数据稀缺的挑战，迁移学习成为关键范式。本文提出了一种面向工业场景的定制化方法——风格过滤器。该方法在知识迁移前对源域数据进行选择性过滤，在保持甚至提升迁移学习策略性能的同时减少数据量。风格过滤器具有无标签操作、极少依赖先验知识、独立于特定模型及可重复利用等特性，通过在真实工业数据集上的评估，证明了其在深度学习领域中作为传统迁移策略前置环节的有效性。实验结果凸显了风格过滤器在实际工业应用中的显著成效。