What does "dropout" refer to in neural networks?

In the context of neural networks, "dropout" refers to a regularization method that randomly ignores a subset of neurons during the training process. This technique helps to prevent overfitting by ensuring that the model does not become overly reliant on any particular set of neurons. By effectively "dropping out" random neurons for each training iteration, dropout encourages the neural network to develop a more robust set of features, which can lead to better generalization performance on unseen data.

The primary purpose of dropout is to create an ensemble effect, as multiple independent models are essentially trained simultaneously. Since different neurons are dropped out each time, the model learns multiple sub-representations of the data, contributing to its ability to generalize well to new examples. As a result, dropout serves as a critical tool in the training of deep learning models, maintaining performance while mitigating the risks associated with overfitting.