Putting MAP Back on the Map

12 years 4 months ago
Putting MAP Back on the Map
Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics.
Patrick Pletscher, Sebastian Nowozin, Pushmeet Koh
Added 18 Dec 2011
Updated 18 Dec 2011
Type Journal
Year 2011
Where DAGM
Authors Patrick Pletscher, Sebastian Nowozin, Pushmeet Kohli, Carsten Rother
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