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Modeling pixel means and covariances using factorized third-order boltzmann machines
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Marc'Aurelio Ranzato, Geoffrey E. Hinton
Issue Date:June 2010
pp. 2551-2558
Learning a generative model of natural images is a useful way of extracting features that capture interesting regularities. Previous work on learning such models has focused on methods in which the latent features are used to determine the mean and varianc...
 
Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Marc'Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, Yann LeCun
Issue Date:June 2007
pp. 1-8
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 of multiple convolution filters, followed by a feature-pooling layer that co...
 
Unsupervised image ranking
Found in: Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining (LS-MMRM '09)
By Eva Horster, Kilian Weinberger, Malcolm Slaney, Marc'Aurelio Ranzato
Issue Date:October 2009
pp. 81-88
In the paper, we propose and test an unsupervised approach for image ranking. Prior solutions are based on image content and the similarity graph connecting images. We generalize this idea by directly estimating the likelihood of each photo in a feature sp...
     
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