Dilution (neural networks)

Dilution and dropout (also called DropConnect) are regularization techniques for reducing overfitting in artificial neural networks by preventing complex co-adaptations on training data. They are an efficient way of performing model averaging with neural networks. Dilution refers to thinning weights, while dropout refers to randomly "dropping out", or omitting, units (both hidden and visible) during the training process of a neural network. Both trigger the same type of regularization.