Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus

arXiv:2403.16607v1 Announce Type: new
Abstract: 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 …

abstract arxiv challenge cost cs.cv cs.lg data domain domains filter filtering focus industrial knowledge methodology paradigm pivotal style transfer transfer learning type work