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| • |
Contrastive
divergence learning can remove the effects
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of the
second-order statistics on the learning without
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actually
changing the data.
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The
lateral connections model the second order
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statistics
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If
a pixel can be reconstructed correctly using second
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order
statistics, its will be the same in the
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reconstruction
as in the data.
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The
hidden units can then focus on modeling high-
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order
structure that cannot be predicted by the lateral
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connections.
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For
example, a pixel close to an edge, where interpolation
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from
nearby pixels causes incorrect smoothing.
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