  Both the Dictionary Learning (DL) and Convolutional Neural Networks (CNN) are
powerful image representation learning systems based on different mechanisms
and principles, however whether we can seamlessly integrate them to improve the
per-formance is noteworthy exploring. To address this issue, we propose a novel
generalized end-to-end representation learning architecture, dubbed
Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which
integrates the learning schemes of the CNN and dictionary pair learning into a
unified framework. Generally, the architecture of CDPL-Net includes two
convolutional/pooling layers and two dictionary pair learn-ing (DPL) layers in
the representation learning module. Besides, it uses two fully-connected layers
as the multi-layer perception layer in the nonlinear classification module. In
particular, the DPL layer can jointly formulate the discriminative synthesis
and analysis representations driven by minimizing the batch based
reconstruction error over the flatted feature maps from the convolution/pooling
layer. Moreover, DPL layer uses l1-norm on the analysis dictionary so that
sparse representation can be delivered, and the embedding process will also be
robust to noise. To speed up the training process of DPL layer, the efficient
stochastic gradient descent is used. Extensive simulations on real databases
show that our CDPL-Net can deliver enhanced performance over other
state-of-the-art methods.
