Dropout as data augmentation

Understand

Dropout is typically interpreted as bagging a large number of models sharing parameters.

• We show that using dropout in a network can also be interpreted as a kind of data augmentation in the input space without domain knowledge.
• We present an approach to projecting the dropout noise within a network back into the input space, thereby generating augmented versions of the training data, and we show that training a deterministic network on the augmented samples yields similar results.
• Finally, we propose a new dropout noise scheme based on our observations and show that it improves dropout results without adding significant computational cost.