论文标题
Deadwooding：深度神经网络的强大全球修剪
Deadwooding: Robust Global Pruning for Deep Neural Networks
论文作者
论文摘要
深度神经网络近似高度复杂功能的能力是其成功的关键。但是，这种好处是以巨大的型号为代价的，这挑战了其在资源受限环境中的部署。修剪是一种用于限制此问题的有效技术，但通常以降低准确性和对抗性鲁棒性为代价。本文解决了这些缺点，并引入了Deadwooding，这是一种新型的全球修剪技术，它利用了Lagrangian双重方法来鼓励模型稀疏性，同时保持准确性并确保鲁棒性。结果模型显示出在鲁棒性和准确性度量方面的最新研究大大优于最先进的研究。
The ability of Deep Neural Networks to approximate highly complex functions is key to their success. This benefit, however, comes at the expense of a large model size, which challenges its deployment in resource-constrained environments. Pruning is an effective technique used to limit this issue, but often comes at the cost of reduced accuracy and adversarial robustness. This paper addresses these shortcomings and introduces Deadwooding, a novel global pruning technique that exploits a Lagrangian Dual method to encourage model sparsity while retaining accuracy and ensuring robustness. The resulting model is shown to significantly outperform the state-of-the-art studies in measures of robustness and accuracy.
