Year: 2018
Type: conference-paper
Abstract: 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.
Authors: Gao Huang, Shichen Liu, Laurens van der Maaten, Kilian Q. Weinberger
Institutions: Cornell University, Tsinghua University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: RI: AF: Small: Collaborative Research: Differentially Private Learning: From Theory to Applications, CAREER: New Directions for Metric Learning, G4504108201, III: Small: Collaborative Research: Towards Interpretable Machine Learning, TRIPODS: Data Science for Improved Decision-Making: Learning in the Context of Uncertainty, Causality, Privacy, and Network Structures, G709468757, S&AS: INT: Inference, Reasoning, and Learning for Robust Autonomous Driving, G8876996369, G8929337198
OpenAlex ID: W2963993763