IEEE Trans Pattern Anal Mach Intell - Modeling Natural Images Using Gated MRFs.

Tópicos

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Resumo

This paper describes a Markov Random Field for real-valued image modeling that has two sets of latent variables. One set is used to gate the interactions between all pairs of pixels while the second set determines the mean intensities of each pixel. This is a powerful model with a conditional distribution over the input that is Gaussian with both mean and covariance determined by the configuration of latent variables, which is unlike previous models that were restricted to use Gaussians with either a fixed mean or a diagonal covariance matrix. Thanks to the increased flexibility, this gated MRF can generate more realistic samples after training on an unconstrained distribution of high-resolution natural images. Furthermore, the latent variables of the model can be inferred efficiently and can be used as very effective descriptors in recognition tasks. Both generation and discrimination drastically improve as layers of binary latent variables are added to the model, yielding a hierarchical model called a Deep Belief Network.

Resumo Limpo

paper describ markov random field realvalu imag model two set latent variabl one set use gate interact pair pixel second set determin mean intens pixel power model condit distribut input gaussian mean covari determin configur latent variabl unlik previous model restrict use gaussian either fix mean diagon covari matrix thank increas flexibl gate mrf can generat realist sampl train unconstrain distribut highresolut natur imag furthermor latent variabl model can infer effici can use effect descriptor recognit task generat discrimin drastic improv layer binari latent variabl ad model yield hierarch model call deep belief network

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