  We present a new Deep Dictionary Learning and Coding Network (DDLCN) for
image recognition tasks with limited data. The proposed DDLCN has most of the
standard deep learning layers (e.g., input/output, pooling, fully connected,
etc.), but the fundamental convolutional layers are replaced by our proposed
compound dictionary learning and coding layers. The dictionary learning learns
an over-complete dictionary for 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. Then the activated dictionary atoms are assembled and
passed to the 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 shared among the input dictionary atoms, thus a
more informative and discriminative low-level representation of the dictionary
atoms can be obtained. We empirically compare DDLCN with several leading
dictionary learning methods and deep learning models. Experimental results on
five popular datasets show that DDLCN achieves competitive results compared
with state-of-the-art methods when the training data is limited. Code is
available at https://github.com/Ha0Tang/DDLCN.
