NeurIPS 2016poster90 citations
Proximal Deep Structured Models
Shenlong Wang, Sanja Fidler, Raquel Urtasun
Abstract
Many problems in real-world applications involve predicting continuous-valued random variables that are statistically related. In this paper, we propose a powerful deep structured model that is able to learn complex non-linear functions which encode the dependencies between continuous output variables. We show that inference in our model using proximal methods can be efficiently solved as a feed-foward pass of a special type of deep recurrent neural network. We demonstrate the effectiveness of our approach in the tasks of image denoising, depth refinement and optical flow estimation.
BibTeX
@inproceedings{NIPS2016_f4be0027,
author = {Wang, Shenlong and Fidler, Sanja and Urtasun, Raquel},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Proximal Deep Structured Models},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/f4be00279ee2e0a53eafdaa94a151e2c-Paper.pdf},
volume = {29},
year = {2016}
}