The presence of noisy labels in a training set can significantly impact the performance of machine learning models. However, there are several techniques to mitigate the effects of noisy labels and improve the model's accuracy. Here are some commonly used approaches:

  1. Data Augmentation: Increasing the size of the training set by introducing modified or transformed versions of existing labeled data can help to reduce the impact of noisy labels. By generating additional samples, the model can learn more robust and generalizable features.

  2. Majority Voting: In cases where there are multiple noisy labels assigned to a single sample, taking the majority vote or average of the labels can be an effective way to mitigate the noise. This assumes that the majority of the labels are correct and can help to correct the mislabeled samples.

  3. Ensemble Methods: Building an ensemble of multiple models can help to reduce the influence of noisy labels. By training multiple models with different initializations or architectures and aggregating their predictions, the noise in the labels can be smoothed out. Techniques like bagging and boosting can be effective in this regard.

  4. Self-training: This semi-supervised learning approach involves using the model's predictions on unlabeled data to obtain additional training samples with confident predictions. The unlabeled data is then labeled using the model's predictions, and this process is iteratively repeated. By enlarging the training set with pseudo-labeled data, the model can learn more robustly.

  5. Noise-resistant Loss Functions: Designing loss functions that are less sensitive to noisy labels can help to mitigate their impact. For example, the "Cross-Entropy Loss with Label Smoothing" technique replaces one-hot encoded labels with a smoothed distribution, allowing the model to learn from noisy labels while still penalizing incorrect predictions.

  6. Active Learning: In an active learning setting, the model actively selects the most informative samples to query their labels from an oracle, such as a domain expert. By focusing on uncertain or ambiguous samples, the model can reduce the influence of noisy labels and improve its generalization performance.

  It is important to note that the effectiveness of these techniques can vary depending on the specific dataset and the nature of the noise in the labels. It is advisable to experiment with different approaches and evaluate their impact on the model's performance.