Neural networks handle noisy data by learning to focus on meaningful patterns while ignoring irrelevant information. During training, the network minimizes the error between its predictions and the actual labels, gradually learning to identify and prioritize features that contribute most to accurate predictions.
Techniques like regularization and data augmentation can improve robustness against noise. For example, dropout randomly deactivates neurons during training, forcing the network to distribute learning across multiple pathways, making it less sensitive to noise. Data augmentation artificially increases dataset diversity by introducing transformations like rotation, scaling, or slight distortions, helping the model become invariant to minor perturbations.
Loss functions and robust optimization strategies also play a role. Loss functions like mean absolute error (MAE) are less sensitive to outliers compared to mean squared error (MSE), making them suitable for noisy data. Similarly, early stopping prevents overfitting to noisy training data by halting training once the validation performance stops improving.