  A generative Bayesian model is developed for deep (multi-layer) convolutional
dictionary learning. A novel probabilistic pooling operation is integrated into
the deep model, yielding efficient bottom-up and top-down probabilistic
learning. After learning the deep convolutional dictionary, testing is
implemented via deconvolutional inference. To speed up this inference, a new
statistical approach is proposed to project the top-layer dictionary elements
to the data level. Following this, only one layer of deconvolution is required
during testing. Experimental results demonstrate powerful capabilities of the
model to learn multi-layer features from images. Excellent classification
results are obtained on both the MNIST and Caltech 101 datasets.
