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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.16607 |
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| _version_ | 1866916175244427264 |
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| author | Li, Chen Ma, Ruijie Qian, Xiang Wang, Xiaohao Li, Xinghui |
| author_facet | Li, Chen Ma, Ruijie Qian, Xiang Wang, Xiaohao Li, Xinghui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16607 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus Li, Chen Ma, Ruijie Qian, Xiang Wang, Xiaohao Li, Xinghui Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.16607 |