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Deep neural networks are often highly overparameterized, prohibiting theiruse in compute-limited systems. However, a line of recent works has shown thatthe size of deep networks can be considerably reduced by identifying a subsetof neuron indicators (or mask) that correspond to significant weights prior totraining. We demonstrate that an simple iterative mask discovery method canachieve state-of-the-art compression of very deep networks. Our algorithmrepresents a hybrid approach between single shot network pruning methods (suchas SNIP) with Lottery-Ticket type approaches. We validate our approach onseveral datasets and outperform several existing pruning approaches in bothtest accuracy and compression ratio.

ESPN: Extremely Sparse Pruned Networks