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.
['Sam Leroux', 'Steven Bohez', 'Cedric De Boom', 'Elias De Coninck', 'Tim Verbelen', 'Bert Vankeirsbilck', 'Pieter Simoens', 'Bart Dhoedt']
Lazy Evaluation of Convolutional Filters