Summary of Noisy Early Stopping For Noisy Labels, by William Toner et al.
Noisy Early Stopping for Noisy Labels
by William Toner, Amos Storkey
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In a machine learning context where datasets are contaminated with noisy labels, effectively implementing early stopping is crucial to prevent overfitting. The traditional approach involves utilizing a validation set free from label noise to monitor generalization during training. However, obtaining such a set can be costly and challenging. This study reveals that in typical learning environments, a noise-free validation set may not be necessary for effective early stopping. Instead, monitoring accuracy on a noisy dataset drawn from the same distribution as the training set can achieve near-optimal results. The proposed method, referred to as Noisy Early Stopping (NES), simplifies and reduces the cost of implementing early stopping. We provide theoretical insights into the conditions under which this method is effective and empirically demonstrate its robust performance across standard benchmarks using common loss functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a world where machines can learn from data, noisy labels in datasets are a big problem. Early stopping helps prevent this issue by monitoring how well the model generalizes during training. But getting a clean validation set to do this is hard and expensive. This study shows that you don’t need a perfect validation set to make early stopping work. Instead, you can use the same noisy data your model was trained on, which makes things simpler and cheaper. The researchers provide some theoretical explanations for when this method works well and show through experiments that it does indeed perform well across various benchmarks. |
Keywords
» Artificial intelligence » Early stopping » Generalization » Machine learning » Overfitting
