Dropout

Dropout is a regularization technique used in neural network training, proposed by Geoffrey Hinton and his team in 2012.Improving neural networks by preventing co-adaptation of feature detectorsIn addition, Hinton et al. published a paper in 2012 titledImageNet Classification with Deep Convolutional Neural NetworksThe Dropout algorithm was also used in ", which also had a profound impact on the field of deep learning.

The core idea of Dropout is to randomly discard (temporarily remove) some neurons in the network and their connections during training to prevent the model from overfitting. In this way, Dropout forces each neuron in the network to be independent of other specific neurons, thereby enhancing the generalization ability of the model.

The key implementation of Dropout is that in each training iteration, each neuron is set to zero with a certain probability (usually a hyperparameter), so that multiple different network structures can be randomly created. At test time, all neurons are activated, but their weights are adjusted according to the probability that they were retained during training. This method can be seen as averaging multiple different network models during training, and using an approximate version of this average model at test time.

When the algorithm was first proposed, it was mainly applied to feedforward neural networks, but subsequent research has expanded its application scope to also apply it to convolutional neural networks (CNN) and recurrent neural networks (RNN). Dropout technology has been proven to be effective in a variety of tasks, including visual recognition, speech recognition, document classification, and computational biology, and has achieved state-of-the-art results on many benchmark datasets.