Surprising properties of dropout in deep networks
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Proceedings of the 2017 Conference on Learning Theory, PMLR 65:11231146, 2017.
Abstract
We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output is the sum of the inputs. This provides a counterpoint to the suggestion that dropout discourages coadaptation of weights. We also show that the dropout penalty can grow exponentially in the depth of the network while the weightdecay penalty remains essentially linear, and that dropout is insensitive to various rescalings of the input features, outputs, and network weights. This last insensitivity implies that there are no isolated local minima of the dropout training criterion. Our work uncovers new properties of dropout, extends our understanding of why dropout succeeds, and lays the foundation for further progress.
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