Summary of Fine-tuning Vision Classifiers on a Budget, by Sunil Kumar et al.
Fine-tuning Vision Classifiers On A Budget
by Sunil Kumar, Ted Sandler, Paulina Varshavskaya
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 approach is proposed for fine-tuning computer vision models when ground truth data may not exist, but multiple labels with varying accuracy can be obtained from labelers. The concept of label quality is tied to the confidence in labeler accuracy, and it is shown that using a simple naive-Bayes model to estimate true labels allows for labeling more data on a fixed budget without compromising label or fine-tuning quality. The proposed method, Ground Truth Extension (GTX), enables fine-tuning of ML models using fewer human labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way has been found to make computer vision models better when we don’t have perfect training data. Instead of just guessing what the correct answer is, this approach uses information from people who are good or bad at labeling things. By taking into account how good or bad each person is, we can use fewer human labels and still get accurate results. This method, called GTX, helps us fine-tune our models using less data. |
Keywords
» Artificial intelligence » Fine tuning » Naive bayes
