Dropout is a popular regularization technique used in training artificial neural networks. Its primary purpose is to prevent overfitting, which occurs when a model learns the training data too well, including its noise and outliers, resulting in poor performance on unseen data.
During the training phase, dropout randomly sets a fraction of the neurons to zero at each iteration. This means that each neuron is temporarily “dropped out” of the network, along with its connections. The fraction of neurons to drop is a hyperparameter, typically set between 20% to 50%. By doing this, dropout forces the network to learn redundant representations of the data, as it cannot rely on any specific neuron for its predictions.
When the model is in the inference phase (i.e., making predictions), dropout is not applied, and all neurons are used. However, the outputs of the neurons are scaled down by the dropout rate to maintain consistency in the expected output. This technique helps in building more robust models that generalize better to new data.
Dropout has been shown to improve performance in a variety of tasks, including image classification and natural language processing. It is easy to implement and has become a standard practice in modern deep learning frameworks.