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.

针对工业领域中数据稀缺的挑战，转移学习成为一种关键的范式。该研究介绍了一种专门针对工业环境的方法学，称为 Style Filter。通过在知识转移之前有选择性地过滤源领域数据，Style Filter 减少了数据的数量，同时保持或增强了转移学习策略的性能。Style Filter 在深度学习领域的传统转移策略之前使用，并在真实工业数据集上进行了评估，突出了其在实际工业应用中的效果。