What are the features of label noise robust training?

Label noise robust training refers to approaches and methods in machine learning that are resistant to label errors in data. In real-world tasks, data often contains incorrect or imprecise labels, which can degrade model quality.

Features of such methods:

• Robustness to incorrect labels: models or algorithms can ignore or minimize the impact of wrong labels.
• Use of special loss functions: e.g., loss functions less sensitive to outliers or label errors.
• Training with weak supervision: using weak or noisy sources of labels, combining multiple sources to improve quality.
• Active learning: selecting the most informative or uncertain examples for re-verification and label refinement.
• Regularization and early stopping: to prevent overfitting on noisy labels.
• Data cleaning methods: automatic detection and correction or removal of erroneous labels.

An example approach is using a noise-robust loss function, such as a modified cross-entropy loss, or training methods that account for label error probabilities.

Thus, label noise robust training helps create more reliable models in noisy, real-world data conditions.