Summary of Trimming the Risk: Towards Reliable Continuous Training For Deep Learning Inspection Systems, by Altaf Allah Abbassi et al.
Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
by Altaf Allah Abbassi, Houssem Ben Braiek, Foutse Khomh, Thomas Reid
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Software Engineering (cs.SE)
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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 paper develops a robust Continuous Training (CT) approach to maintain the effectiveness of deep learning (DL)-powered inspection systems in manufacturing environments. The method uses a two-stage filtering process to select reliable data for updating DL models, which is crucial to mitigate the risk of silent performance degradation due to self-generated labels. This strategy ensures the model adapts effectively to new operational conditions while maintaining its original performance on validation data. Evaluations on industrial inspection systems for popsicle stick prints and glass bottles demonstrate improved model performance on production data by up to 14% without compromising results on original validation data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a way to keep deep learning models used in manufacturing inspections updated and accurate. This is important because the models can become less good at detecting defects if they’re only trained on old data. The researchers developed a method that filters out bad data and uses reliable data to fine-tune the model. They tested this approach on real-world datasets for inspecting popsicle stick prints and glass bottles, and it improved the model’s performance by up to 14%. |
Keywords
» Artificial intelligence » Deep learning
