  Dropout is commonly used to help reduce overfitting in deep neural networks.
Sparsity is a potentially important property of neural networks, but is not
explicitly controlled by Dropout-based regularization. In this work, we propose
Sparseout a simple and efficient variant of Dropout that can be used to control
the sparsity of the activations in a neural network. We theoretically prove
that Sparseout is equivalent to an $L_q$ penalty on the features of a
generalized linear model and that Dropout is a special case of Sparseout for
neural networks. We empirically demonstrate that Sparseout is computationally
inexpensive and is able to control the desired level of sparsity in the
activations. We evaluated Sparseout on image classification and language
modelling tasks to see the effect of sparsity on these tasks. We found that
sparsity of the activations is favorable for language modelling performance
while image classification benefits from denser activations. Sparseout provides
a way to investigate sparsity in state-of-the-art deep learning models. Source
code for Sparseout could be found at
\url{https://github.com/najeebkhan/sparseout}.
