Training Compact DNNs with [formula omitted] Regularization

Abstract

Deep neural network(DNN) has achieved unprecedented success in many fields. However, its large model parameters which bring a great burden on storage and calculation hinder the development and application of DNNs. It is worthy of compressing the model to reduce the complexity of the DNN. Sparsity-inducing regularizer is one of the most common tools for compression. In this paper, we propose utilizing the ℓ1/2 quasi-norm to zero out weights of neural networks and compressing the networks automatically during the learning process. To our knowledge, it is the first work applying the non-Lipschitz continuous regularizer for the compression of DNNs. The resulting sparse optimization problem is solved by stochastic proximal gradient algorithm. For further convenience of calculation, an approximation of the threshold-form solution to the proximal operator with ℓ1/2 is given at the same time. Extensive experiments with various datasets and baselines demonstrate the advantages of our new method.