Training Sparse Neural Networks using Compressed Sensing

• URL: http://arxiv.org/abs/2008.09661v2
• Date: Wed, 7 Apr 2021 04:14:08 GMT
• Title: Training Sparse Neural Networks using Compressed Sensing
• Authors: Jonathan W. Siegel, Jianhong Chen, Pengchuan Zhang, Jinchao Xu
• Abstract summary: We develop and test a novel method based on compressed sensing which combines the pruning and training into a single step. Specifically, we utilize an adaptively weighted $ell1$ penalty on the weights during training, which we combine with a generalization of the regularized dual averaging (RDA) algorithm in order to train sparse neural networks.
• Score: 13.84396596420605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pruning the weights of neural networks is an effective and widely-used technique for reducing model size and inference complexity. We develop and test a novel method based on compressed sensing which combines the pruning and training into a single step. Specifically, we utilize an adaptively weighted $\ell^1$ penalty on the weights during training, which we combine with a generalization of the regularized dual averaging (RDA) algorithm in order to train sparse neural networks. The adaptive weighting we introduce corresponds to a novel regularizer based on the logarithm of the absolute value of the weights. We perform a series of ablation studies demonstrating the improvement provided by the adaptive weighting and generalized RDA algorithm. Furthermore, numerical experiments on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that our method 1) trains sparser, more accurate networks than existing state-of-the-art methods; 2) can be used to train sparse networks from scratch, i.e. from a random initialization, as opposed to initializing with a well-trained base model; 3) acts as an effective regularizer, improving generalization accuracy.

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