CondenseNet: An Efficient DenseNet using Learned Group Convolutions
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Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architecture with unprecedented efficiency. It combines dense connectivity between layers with a mechanism to remove unused connections. The dense connectivity facilitates feature re-use in the network, whereas learned group convolutions remove connections between layers for which this feature re-use is superfluous. At test time, our model can be implemented using standard grouped convolutions - allowing for efficient computation in practice. Our experiments demonstrate that CondenseNets are much more efficient than stateof-the-art compact convolutional networks such as MobileNets and ShuffleNets.
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