Schematic of how the super-resolution CNN was trained (IMAGE)

Tokyo Metropolitan University

Schematic of how the super-resolution CNN was trained

Caption

A correlation configuration (top left) is reduced using a newly developed block-cluster transformation (top right). Both the original and reduced configurations have an improved estimator technique applied to give configuration pairs of different size (bottom row). Using these training pairs, a CNN can learn to convert small patterns to large ones, achieving a successful inverse RG transformation.

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Tokyo Metropolitan University

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