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Both the Dictionary Learning (DL) and Convolutional Neural Networks (CNN) arepowerful image representation learning systems based on different mechanismsand principles, however whether we can seamlessly integrate them to improve theper-formance is noteworthy exploring. To address this issue, we propose a novelgeneralized end-to-end representation learning architecture, dubbedConvolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, whichintegrates the learning schemes of the CNN and dictionary pair learning into aunified framework. Generally, the architecture of CDPL-Net includes twoconvolutional/pooling layers and two dictionary pair learn-ing (DPL) layers inthe representation learning module. Besides, it uses two fully-connected layersas the multi-layer perception layer in the nonlinear classification module. Inparticular, the DPL layer can jointly formulate the discriminative synthesisand analysis representations driven by minimizing the batch basedreconstruction error over the flatted feature maps from the convolution/poolinglayer. Moreover, DPL layer uses l1-norm on the analysis dictionary so thatsparse representation can be delivered, and the embedding process will also berobust to noise. To speed up the training process of DPL layer, the efficientstochastic gradient descent is used. Extensive simulations on real databasesshow that our CDPL-Net can deliver enhanced performance over otherstate-of-the-art methods.

Convolutional Dictionary Pair Learning Network for Image Representation Learning