Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions

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Heng Luo, Pierre Luc Carrier, Aaron Courville, Yoshua Bengio ;
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, PMLR 31:415-423, 2013.

Abstract

We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and binary variables in the hidden layer. This model is designed to be stacked on top of the ssRBM. We show the resulting deep belief network (DBN) is a powerful generative model that improves on single-layer models and is capable of modeling not only single high-resolution and challenging textures but also multiple textures with fixed-size filters in the bottom layer.

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