  A deep generative model is developed for representation and analysis of
images, based on a hierarchical convolutional dictionary-learning framework.
Stochastic {\em unpooling} is employed to link consecutive layers in the model,
yielding top-down image generation. A Bayesian support vector machine is linked
to the top-layer features, yielding max-margin discrimination. Deep
deconvolutional inference is employed when testing, to infer the latent
features, and the top-layer features are connected with the max-margin
classifier for discrimination tasks. The model is efficiently trained using a
Monte Carlo expectation-maximization (MCEM) algorithm, with implementation on
graphical processor units (GPUs) for efficient large-scale learning, and fast
testing. Excellent results are obtained on several benchmark datasets,
including ImageNet, demonstrating that the proposed model achieves results that
are highly competitive with similarly sized convolutional neural networks.
