Inverse Renormalization Group based on Image Super-Resolution using Deep Convolutional Networks

Understand

The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks.

• We consider the improved correlation configuration instead of spin configuration for the spin models, such as the two-dimensional Ising and three-state Potts models.
• We propose a block-cluster transformation as an alternative to the block-spin transformation in dealing with the improved estimators.
• In the framework of the dual Monte Carlo algorithm, the block-cluster transformation is regarded as a transformation in the graph degrees of freedom, whereas the block-spin transformation is that in the spin degrees of freedom.