  We describe and analyze a new boosting algorithm for deep learning called
SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct
ensembles of classifiers, SelfieBoost boosts the accuracy of a single network.
We prove a $\log(1/\epsilon)$ convergence rate for SelfieBoost under some "SGD
success" assumption which seems to hold in practice.
