Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and

Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks

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.

深度转换和度量学习网络：结合深度词典学习和神经网络

由于其在推理任务和去噪应用方面的许多成功，Dictionary Learning（DL）及其相关的稀疏优化问题引起了很多研究兴趣。尽管大多数解决方案都集中在单层词典上，但是最近提出的改进的Deep DL（DDL）方法在许多问题上也很不足。.. 我们在本文中提出了一种新颖的DDL方法，其中每个DL层都可以公式化为一个线性层和递归神经网络（RNN）的组合。RNN被显示为灵活地考虑了与层相关的学习指标。我们提出的工作揭示了对神经网络和DDL的新见解，并提供了一种新的，有效的和竞争性的方法来共同学习深度转换和推理应用的度量标准。进行了广泛的实验，证明了该方法不仅可以胜过现有的DDL，而且还可以胜任最新的通用CNN。 （阅读更多）