From the 1 of 140 linked papers with an AI index.
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Super-Resolution with Deep Convolutional Sufficient Statistics
Joan Bruna, Pablo Sprechmann, Yann LeCun
Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the condi…
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Jure Žbontar, Yann LeCun
We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation.…
Learning to Linearize Under Uncertainty
Ross Goroshin, Michael Mathieu, Yann LeCun
Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. However, a principled way in whi…
Unsupervised Feature Learning from Temporal Data
Ross Goroshin, Joan Bruna, Jonathan Tompson +2
Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coher…