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 with a novel module called learned group convolution. 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 group convolutions, allowing for efficient computation in practice. Our experiments show that Con-denseNets are far more efficient than state-of-the-art compact convolutional networks such as ShuffleNets.
CondenseNet REF learns group convolutions at the training stage to keep useful dense connections between layers for feature re-use.
CondenseNet: An Efficient DenseNet Using Learned Group Convolutions
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science