[Lisa_teatalk] TalkTalk Reminder: Recombinator Networks in Z-209!

Jörg Bornschein bornj at iro.umontreal.ca
Fri Jan 15 14:05:54 EST 2016


Hi, just friendly reminder:

The tea talk will be at 14:30  in

   Z-209 Pavilion Mc Nicoll

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Title: Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Where: Z-209 Pavilion Mc Nicoll
When: Fri.,  Jan. 15th 2016, 14:30
Who: Sina Honari
Link: http://arxiv.org/abs/1511.07356

Abstract:

Deep neural networks with alternating convolutional, max-pooling and
decimation layers are widely used in state of the art architectures for
computer vision. Max-pooling purposefully discards precise spatial
information in order to create features that are more robust, and typically
organized as lower resolution spatial feature maps. On some tasks, such as
whole-image classification, max-pooling derived features are well suited;
however, for tasks requiring precise localization, such as pixel level
prediction and segmentation, max-pooling destroys exactly the information
required to perform well. Precise localization may be preserved by shallow
convnets without pooling but at the expense of robustness. Can we have our
max-pooled multi-layered cake and eat it too? Several papers have proposed
summation and concatenation based methods for combining upsampled coarse,
abstract features with finer features to produce robust pixel level
predictions. Here we introduce another model --- dubbed Recombinator
Networks --- where coarse features inform finer features early in their
formation such that finer features can make use of several layers of
computation in deciding how to use coarse features. The model is trained
once, end-to-end and performs better than summation-based architectures,
reducing the error from the previous state of the art on two facial
keypoint datasets, AFW and AFLW, by 30% and beating the current
state-of-the-art on 300W without using extra data. We improve performance
even further by adding a denoising prediction model based on a novel
convnet formulation.
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Looking forward to see you there!


   j
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