论文标题
如何不给flop：结合正则化和修剪以有效推断
How Not to Give a FLOP: Combining Regularization and Pruning for Efficient Inference
论文作者
论文摘要
在部署阶段，加快深度学习模型的挑战是现代科技行业的巨大，昂贵的瓶颈。在本文中，我们研究了在深度神经网络（DNN）中降低计算复杂性和更有效的推断的正则化和修剪的使用。特别是，我们将混合和切割的正规化和软滤清器修剪应用于重新系统架构，重点是最大程度地减少浮点操作（FLOPS）。此外，通过将正则化与网络修剪结合使用，我们表明，这种组合可以单独地对两种技术中的每种技术进行了重大改进。
The challenge of speeding up deep learning models during the deployment phase has been a large, expensive bottleneck in the modern tech industry. In this paper, we examine the use of both regularization and pruning for reduced computational complexity and more efficient inference in Deep Neural Networks (DNNs). In particular, we apply mixup and cutout regularizations and soft filter pruning to the ResNet architecture, focusing on minimizing floating-point operations (FLOPs). Furthermore, by using regularization in conjunction with network pruning, we show that such a combination makes a substantial improvement over each of the two techniques individually.
