Deep Network Shrinkage Applied to Cross-Spectrum Face Recognition

摘要

In recent years, deep learning has emerged as a dominant methodology in virtually all machine learning problems. While it has been shown to produce state-of-the-art results for a variety of applicatons (including face recognition and heterogeneous face recognition), one aspect of deep networks that has not been extensively researched is how to determine the optimal network structure. This problem is generally solved by ad hoc methods. In this work we address a subproblem of this task: determining the breadth (number of nodes) of each layer. We show how to use group-sparsity-inducing regularization to effectively replace these hyper-parameters with a single hyperparameter which can be determined by cross-validation. We demonstrate our method by using it to reduce the size of networks on two commonly used NIR face datasets.