Clustering Learning for Robotic Vision
Computer Vision and Pattern Recognition
2013-03-15 v3
Abstract
We present the clustering learning technique applied to multi-layer feedforward deep neural networks. We show that this unsupervised learning technique can compute network filters with only a few minutes and a much reduced set of parameters. The goal of this paper is to promote the technique for general-purpose robotic vision systems. We report its use in static image datasets and object tracking datasets. We show that networks trained with clustering learning can outperform large networks trained for many hours on complex datasets.
Cite
@article{arxiv.1301.2820,
title = {Clustering Learning for Robotic Vision},
author = {Eugenio Culurciello and Jordan Bates and Aysegul Dundar and Jose Carrasco and Clement Farabet},
journal= {arXiv preprint arXiv:1301.2820},
year = {2013}
}
Comments
Code for this paper is available here: https://github.com/culurciello/CL_paper1_code