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Creators/Authors contains: "Gao Huang, Shichen Liu"

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  1. (, CVPR)
    Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architec- ture with unprecedented efficiency. It combines dense con- nectivity between layers with a mechanism to remove un- used connections. The dense connectivity facilitates feature re-use in the network, whereas learned group convolution- s remove connections between layers for which this feature re-use is superfluous. At test time, our model can be imple- mented using standard grouped convolutions—allowing for efficient computation in practice. Our experiments demon- strate that CondenseNets are much more efficient than state- of-the-art compact convolutional networks such as Mo- bileNets and ShuffleNets. 
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    Accepted Manuscript Full Text Available