Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling

Understand

This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images.

• For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN).
• Learning such a model requires generating synthesized examples from the model.
• Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image.