Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction
  and Defect-Focus
Chen Li Ruijie Ma Xiang Qian Xiaohao Wang Xinghui Li
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.