Summary of Co-training Partial Domain Adaptation Networks For Industrial Fault Diagnosis, by Gecheng Chen
Co-training partial domain adaptation networks for industrial Fault Diagnosis
by Gecheng Chen
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel partial domain adaptation (PDA) framework called Interactive Residual Domain Adaptation Networks (IRDAN) is proposed to tackle the PDA challenge in industrial fault diagnosis, which typically involves drawing insights from traditional classification settings where partial domain adaptation is not a concern. IRDAN introduces domain-wise models for each domain, equipped with residual domain adaptation (RDA) blocks to mitigate the adverse domain shift problem (ADP). An interactive learning strategy trains modules sequentially to avoid cross-interference and a reliable stopping criterion selects the best-performing model, ensuring practical usability in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to help machines learn from different kinds of data. This is important for things like diagnosing problems with machines in factories. The problem is that machines often have trouble learning from data that looks very different from what they’re used to. A special kind of network, called IRDAN, is designed to help solve this problem. It has two main parts: a part that helps each type of data learn separately and another part that makes sure the information flows between them correctly. This new way works better than other methods and can be used in real-life situations. |
Keywords
» Artificial intelligence » Classification » Domain adaptation
