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Lazy Evaluation of Convolutional Filters
[article]
2016
arXiv
pre-print
In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network. This allows to trade-off the accuracy of a deep neural network with the computational and memory requirements. This is especially important on a constrained device unable to hold all the weights of the network in memory.
arXiv:1605.08543v1
fatcat:wjrsd4nmwnaafd23z3eusgsmye