Learning deep structured models

Liang Chieh Chen, Alexander G. Schwing, Alan L. Yuille, Raquel Urtasun

Research output: Contribution to conferencePaperpeer-review

Abstract

Many problems in real-world applications involve predicting several random variables which are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such relationships. The goal of this paper is to combine MRFs with deep learning algorithms to estimate complex representations while taking into account the dependencies between the output random variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials. Our approach is efficient as it blends learning and inference and makes use of GPU acceleration. We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as multi-class classification of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains.

Original languageEnglish (US)
StatePublished - 2015
Externally publishedYes
Event3rd International Conference on Learning Representations, ICLR 2015 - San Diego, United States
Duration: May 7 2015May 9 2015

Conference

Conference3rd International Conference on Learning Representations, ICLR 2015
Country/TerritoryUnited States
CitySan Diego
Period5/7/155/9/15

ASJC Scopus subject areas

  • Education
  • Linguistics and Language
  • Language and Linguistics
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Learning deep structured models'. Together they form a unique fingerprint.

Cite this