What is the main purpose of dropout layers in neural networks?

Dropout layers are primarily used to prevent overfitting in neural networks. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers, which negatively impacts its performance on unseen data. During training, dropout randomly disables a fraction of the neurons in the network at each iteration, which forces the remaining neurons to adapt and learn robust features that are not reliant on any particular subset of the network. This creates a more generalized model that performs better on new, unseen data.

This technique encourages the network to learn multiple independent representations of the data, further enhancing generalization. By doing so, dropout effectively reduces the chances of the model memorizing the training set, leading to improved performance on validation and test sets. The other options, such as increasing dataset size, improving computational efficiency, and enabling faster training, do not capture the principal function of dropout layers, which is fundamentally about enhancing model robustness against overfitting.