Texture Synthesis: From Convolutional RBMs to Efficient Deterministic Algorithms

@inproceedings{Gao2014TextureSF,
  title={Texture Synthesis: From Convolutional RBMs to Efficient Deterministic Algorithms},
  author={Qi Gao and Stefan Roth},
  booktitle={S+SSPR},
  year={2014}
}
Probabilistic models of textures should be able to synthesize specific textural structures, prompting the use of filter-based Markov random fields (MRFs) with multi-modal potentials, or of advanced variants of restricted Boltzmann machines (RBMs). However, these complex models have practical problems, such as inefficient inference, or their large number of model parameters. We show how to train a Gaussian RBM with full-convolutional weight sharing for modeling repetitive textures. Since… CONTINUE READING
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