Speedup of deep learning ensembles for semantic segmentation using a model compression technique

Citation data:

Computer Vision and Image Understanding, ISSN: 1077-3142

Publication Year:
2017
Social Media 27
Shares, Likes & Comments 26
Tweets 1
DOI:
10.1016/j.cviu.2017.05.004
Author(s):
Andrew Holliday, Mohammadamin Barekatain, Johannes Laurmaa, Chetak Kandaswamy, Helmut Prendinger
Publisher(s):
Elsevier BV
Tags:
Computer Science
Most Recent Tweet View All Tweets
article description
Deep Learning (DL) has been proven as a powerful recognition method as evidenced by its success in recent computer vision competitions. The most accurate results have been obtained by ensembles of DL models that pool their results. However, such ensembles are computationally costly, making them inapplicable to real-time applications. In this paper, we apply model compression techniques to the problem of semantic segmentation, which is one of the most challenging problems in computer vision. Our results suggest that compressed models can approach the accuracy of full ensembles on this task, combining the diverse strengths of networks of very different architectures, while maintaining real-time performance.

This article has 0 Wikipedia mention.