On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest. While most solutions have focused on single layer dictionaries, the improved recently proposed Deep DL (DDL) methods have also fallen short on a number of issues. We propose herein, a novel DDL approach where each DL layer can be formulated as a combination of one linear layer and a Recurrent Neural Network (RNN). The RNN is shown to flexibly account for the layer-associated and learned metric. Our proposed work unveils new insights into Neural Networks and DDL and provides a new, efficient and competitive approach to jointly learn a deep transform and a metric for inference applications. Extensive experiments are carried out to demonstrate that the proposed method can not only outperform existing DDL but also state-of-the-art generic CNNs.
@article{arxiv.2002.07898,
title = {Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks},
author = {Wen Tang and Emilie Chouzenoux and Jean-Christophe Pesquet and Hamid Krim},
journal= {arXiv preprint arXiv:2002.07898},
year = {2020}
}