Summary of Improving Label Error Detection and Elimination with Uncertainty Quantification, by Johannes Jakubik et al.
Improving Label Error Detection and Elimination with Uncertainty Quantification
by Johannes Jakubik, Michael Vössing, Manil Maskey, Christopher Wölfle, Gerhard Satzger
First submitted to arxiv on: 15 May 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 This paper introduces Uncertainty Quantification-Based Label Error Detection (UQ-LED) algorithms for enhancing the accuracy of supervised machine learning models. Recent approaches demonstrate that low model self-confidence indicates erroneous labels, but softmax probabilities are not accurate uncertainty measures. The authors develop novel, model-agnostic UQ-LED algorithms combining confident learning, Monte Carlo Dropout, entropy, and ensemble learning to detect label errors. Four image classification benchmark datasets are used for comprehensive evaluation, with results showing UQ-LED outperforming state-of-the-art confident learning in identifying errors. Moreover, the study demonstrates that selectively cleaning datasets with UQ-LED leads to more accurate classifications than using larger, noisier datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making machine learning models more accurate by finding and fixing mistakes in their training data. Right now, some approaches rely on how confident a model is in its predictions, but this isn’t the best way to measure uncertainty. The authors come up with new methods that combine different techniques to detect errors in labels (the correct answers). They test these methods on four big datasets of images and show that they work better than current methods. By cleaning up the mistakes in the training data, the models become more accurate. |
Keywords
» Artificial intelligence » Dropout » Image classification » Machine learning » Softmax » Supervised
