Paper
Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or sur-passes the state-of-the-art on texture synthe-sis 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 model-ing not only single high-resolution and chal-lenging textures but also multiple textures with fixed-size filters in the bottom layer. 1
Authors: Heng Luo · Pierre Carrier · Aaron Courville · Yoshua Bengio