  Dropout methods are a family of stochastic techniques used in neural network
training or inference that have generated significant research interest and are
widely used in practice. They have been successfully applied in neural network
regularization, model compression, and in measuring the uncertainty of neural
network outputs. While original formulated for dense neural network layers,
recent advances have made dropout methods also applicable to convolutional and
recurrent neural network layers. This paper summarizes the history of dropout
methods, their various applications, and current areas of research interest.
Important proposed methods are described in additional detail.
