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188
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CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
17 years 1 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
161
Voted
CVPR
2007
IEEE
16 years 8 months ago
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
We present an unsupervised method for learning a hierarchy of sparse feature detectors that are invariant to small shifts and distortions. The resulting feature extractor consists...
Marc'Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau,...
171
Voted
ICDAR
2003
IEEE
15 years 11 months ago
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
Neural networks are a powerful technology for classification of visual inputs arising from documents. However, there is a confusing plethora of different neural network methods th...
Patrice Simard, David Steinkraus, John C. Platt
217
Voted
PAMI
2012
13 years 8 months ago
Task-Driven Dictionary Learning
—Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal proce...
Julien Mairal, Francis Bach, Jean Ponce
176
Voted
ICPR
2006
IEEE
16 years 7 months ago
Supervised Image Classification by SOM Activity Map Comparison
This article presents a method aiming at quantifying the visual similarity between two images. This kind of problem is recurrent in many applications such as object recognition, i...
Grégoire Lefebvre, Christophe Laurent, Juli...