How does regularization contribute to model generalization?

Regularization plays a crucial role in enhancing model generalization by helping to prevent overfitting, which occurs when a model learns to recognize noise and outliers in the training data rather than the underlying patterns. One effective regularization technique is the incorporation of dropout layers in neural networks.

Dropout works by randomly setting a fraction of the input units to zero during training. This process effectively prevents the model from relying too heavily on any particular feature or neuron, promoting a more robust feature representation. As a result, the model learns to maintain its performance even when faced with unseen data, thus improving its generalization capabilities.

While increasing model complexity can sometimes aid in fitting the training data better, it may lead to overfitting rather than improving generalization. Adding random noise to the data could serve as a form of data augmentation, but it doesn't directly function as regularization. Lastly, dynamically changing the learning rate can optimize training efficiency but does not address overfitting and is not classified as a regularization method. Therefore, the use of dropout layers directly contributes to improved generalization by mitigating overfitting through a more balanced learning process.