LAUSR

Abstract Deep learning, especially Convolutional Neural Networks (CNNs), has been widely applied in many domains. The large number of parameters in a CNN allow it to learn complex features, however, they may tend to hinder generalization by over-fitting training data. Despite many previously proposed regularization methods, over-fitting is still a problem in training a robust CNN. Among many factors that lead to over-fitting, the numerous parameters of fully-connected layers (FCLs) of a typical CNN should be taken into account. This paper proposes the SparseConnect, a simple idea which alleviates over-fitting by sparsifying connections to FCLs. Experimental results on three benchmark datasets MNIST, CIFAR10 and ImageNet show that the SparseConnect outperforms several state-of-the-art regularization methods.