Abstract:Deep Convolutional Neural Networks (CNNs) are increasingly used in multimedia retrieval, and accelerating Deep CNNs has recently received an ever-increasing research focus. Among various approaches proposed in the literature, filter pruning has been regarded as a promising solution, which is due to its advantage in significant speedup and memory reduction of both network model and intermediate feature maps. Many works have been proposed to find unimportant filters, and then prune it for accelerating Deep CNNs.… Show more
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