Xavier Snelgrove
Portals
Abstract
We introduce a novel multi-scale approach for synthesizing high-resolution natural textures using convolutional neural networks trained on image classification tasks. Previous breakthroughs were based on the observation that correlations between features at intermediate layers of the network are a powerful texture representation, however the fixed receptive field of network neurons limits the maximum size of texture features that can be synthesized. We show that rather than matching statistical properties at many layers of the CNN, better results can be achieved by matching a small number of network layers but across many scales of a Gaussian pyramid. This leads to qualitatively superior synthesized high-resolution textures.
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