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Poster
Proximal Deep Structured Models
Shenlong Wang · Sanja Fidler · Raquel Urtasun

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #80 #None
in Wednesday Posters »

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.

Keywords: Deep Learning or Neural Networks · Structured Prediction · (Application) Computer Vision
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Author Information

Shenlong Wang (University of Toronto)
Sanja Fidler (University of Toronto)
Raquel Urtasun (University of Toronto)

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