  In this paper, we propose a novel Deep Micro-Dictionary Learning and Coding
Network (DDLCN). DDLCN has most of the standard deep learning layers (pooling,
fully, connected, input/output, etc.) but the main difference is that the
fundamental convolutional layers are replaced by novel compound dictionary
learning and coding layers. The dictionary learning layer learns an
over-complete dictionary for the input training data. At the deep coding layer,
a locality constraint is added to guarantee that the activated dictionary bases
are close to each other. Next, the activated dictionary atoms are assembled
together and passed to the next compound dictionary learning and coding layers.
In this way, the activated atoms in the first layer can be represented by the
deeper atoms in the second dictionary. Intuitively, the second dictionary is
designed to learn the fine-grained components which are shared among the input
dictionary atoms. In this way, a more informative and discriminative low-level
representation of the dictionary atoms can be obtained. We empirically compare
the proposed DDLCN with several dictionary learning methods and deep learning
architectures. The experimental results on four popular benchmark datasets
demonstrate that the proposed DDLCN achieves competitive results compared with
state-of-the-art approaches.
