CondenseNet: An Efficient DenseNet using Learned Group Convolutions

Computer Vision and Pattern Recognition (CVPR)

By: Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger


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 CondenseNets are far more efficient than state-of-the-art compact convolutional networks such as ShuffleNets.