  Dropout is a popular regularization technique in deep learning. Yet, the
reason for its success is still not fully understood. This paper provides a new
interpretation of Dropout from a frame theory perspective. By drawing a
connection to recent developments in analog channel coding, we suggest that for
a certain family of autoencoders with a linear encoder, optimizing the encoder
with dropout regularization leads to an equiangular tight frame (ETF). Since
this optimization is non-convex, we add another regularization that promotes
such structures by minimizing the cross-correlation between filters in the
network. We demonstrate its applicability in convolutional and fully connected
layers in both feed-forward and recurrent networks. All these results suggest
that there is indeed a relationship between dropout and ETF structure of the
regularized linear operations.
