Summary of Image-based Novel Fault Detection with Deep Learning Classifiers Using Hierarchical Labels, by Nurettin Sergin et al.
Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels
by Nurettin Sergin, Jiayu Huang, Tzyy-Shuh Chang, Hao Yan
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed methodology leverages soft label techniques to enhance deep neural network-based fault classifiers’ unknown fault detection capabilities without compromising model performance. By utilizing taxonomy labels during training, the approach improves novel fault detection statistics for online detection. Experimental results demonstrate increased detection performance on inspection images from hot steel rolling processes, with consistent results across multiple scenarios and baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to make deep learning-based fault classifiers better at detecting unknown faults in real-world systems. It shows that by using special labels during training, the classifiers can learn to identify new types of faults more accurately without losing their ability to recognize familiar ones. This could be useful in industries like manufacturing where machines need to detect unusual problems. |
Keywords
* Artificial intelligence * Deep learning * Neural network
