Summary of Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-focus, by Chen Li et al.
Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus
by Chen Li, Ruijie Ma, Xiang Qian, Xiaohao Wang, Xinghui Li
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology, Style Filter, tackles the challenge of data scarcity in industrial domains by selectively filtering source domain data before knowledge transfer. This approach reduces the amount of required data while maintaining or improving performance. Style Filter is label-free, requires minimal prior knowledge, and can be used with various models. The method’s effectiveness is demonstrated on real-world industrial datasets, showing promising results when combined with conventional deep learning strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Style Filter is a new way to help machines learn from limited data in industries. It takes some information from one place and removes unwanted details before sharing it with another place. This helps keep the important parts of the information while getting rid of what’s not needed. Style Filter doesn’t need labels or prior knowledge, making it useful for many different models. The results show that this method can be very effective in real-world industrial applications. |
Keywords
* Artificial intelligence * Deep learning
