Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus
This work addresses data scarcity for industrial applications, but it appears incremental as it builds on existing transfer learning strategies.
The paper tackles data scarcity in industrial domains by introducing Style Filter, a method that selectively filters source domain data before transfer learning, reducing data quantity while maintaining or enhancing performance, as evaluated on authentic industrial datasets.
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.