论文标题
修剪对模型准确性有不同的影响
Pruning has a disparate impact on model accuracy
论文作者
论文摘要
网络修剪是一种广泛使用的压缩技术，能够以最小的准确性损失显着缩小过度参数化模型。本文表明，修剪可能会产生或加剧不同的影响。该论文阐明了导致这种差异的因素，表明梯度规范的差异以及跨组的决策边界的距离造成了这一关键问题。它详细分析了这些因素，提供了理论和经验支持，并提出了一种简单而有效的解决方案，可以减轻修剪造成的不同影响。
Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that pruning may create or exacerbate disparate impacts. The paper sheds light on the factors to cause such disparities, suggesting differences in gradient norms and distance to decision boundary across groups to be responsible for this critical issue. It analyzes these factors in detail, providing both theoretical and empirical support, and proposes a simple, yet effective, solution that mitigates the disparate impacts caused by pruning.
