Poster
Proximal Deep Structured Models
Shenlong Wang · Sanja Fidler · Raquel Urtasun
Area 5+6+7+8 #80
Keywords: [ Structured Prediction ] [ (Application) Computer Vision ] [ Deep Learning or Neural Networks ]
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.
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