论文标题
简化的深神经网络修剪技术的建模
Modeling of Pruning Techniques for Deep Neural Networks Simplification
论文作者
论文摘要
卷积神经网络（CNN）遭受了不同的问题，例如计算复杂性和参数数量。近年来，使用修剪技术来减少CNN中的操作数量和模型大小。提出了不同的修剪方法，这些方法基于修剪连接，通道和过滤器。修剪方法伴随着各种技术和技巧，并且没有一个统一的框架来建模所有修剪方法。在本文中，研究了修剪方法，并提出了大多数修剪技术的一般模型。可以确定修剪方法的优势和缺点，并且可以在此模型下总结所有这些方法。该模型的最终目标是为具有不同结构和应用的所有修剪方法提供一般方法。
Convolutional Neural Networks (CNNs) suffer from different issues, such as computational complexity and the number of parameters. In recent years pruning techniques are employed to reduce the number of operations and model size in CNNs. Different pruning methods are proposed, which are based on pruning the connections, channels, and filters. Various techniques and tricks accompany pruning methods, and there is not a unifying framework to model all the pruning methods. In this paper pruning methods are investigated, and a general model which is contained the majority of pruning techniques is proposed. The advantages and disadvantages of the pruning methods can be identified, and all of them can be summarized under this model. The final goal of this model is to provide a general approach for all of the pruning methods with different structures and applications.
