Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

CVPR 2016 Chuan LiMichael Wand

This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controling the image layout at an abstract level... (read more)

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