Summary of Calibration Of Network Confidence For Unsupervised Domain Adaptation Using Estimated Accuracy, by Coby Penso and Jacob Goldberger
Calibration of Network Confidence for Unsupervised Domain Adaptation Using Estimated Accuracy
by Coby Penso, Jacob Goldberger
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 This study addresses the problem of calibrating network confidence when adapting a model trained on one domain to another using unlabeled target domain samples. The proposed calibration procedure estimates the network’s accuracy on the target domain by modifying its accuracy computed on labeled source data. The algorithm minimizes the disparity between estimated accuracy and predicted confidence, resulting in significantly better performance compared to existing methods like importance weighting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers tackled a tricky problem: calibrating a model that was trained on one place (the “source” domain) but needs to work well in another place (the “target” domain). They didn’t have any labels from the target domain, so they came up with a clever way to estimate how accurate the model is on that new data. This helps the model make better predictions and choose the right confidence level. The study shows that this approach works much better than previous methods. |
