Deep neural networks (DNNs) have demonstrated remarkable success in various fields. However, the large number of floating-point operations (FLOPs) in DNNs poses challenges for their deployment in resource-constrained applications, e.g., edge devices. To address the problem, pruning has been introduced to reduce the computational cost in executing DNNs. Previous pruning strategies are based on weight values, gradient values and activation outputs. Different from previous pruning solutions, in this paper, we propose a class-aware pruning technique to compress DNNs, which provides a novel perspective to reduce the computational cost of DNNs. In each iteration, the neural network training is modified to facilitate the class-aware pruning. Afterwards, the importance of filters with respect to the number of classes is evaluated. The filters that are only important for a few number of classes are removed. The neural network is then retrained to compensate for the incurred accuracy loss. The pruning iterations end until no filter can be removed anymore, indicating that the remaining filters are very important for many classes. This pruning technique outperforms previous pruning solutions in terms of accuracy, pruning ratio and the reduction of FLOPs. Experimental results confirm that this class-aware pruning technique can significantly reduce the number of weights and FLOPs, while maintaining a high inference accuracy.

深度神经网络（DNNs）在各个领域取得了显著的成功。然而，DNNs中的大量浮点运算（FLOPs）对于在资源受限应用中部署它们提出了挑战。为了解决这个问题，引入了修剪技术来减少执行DNNs的计算成本。与以往的修剪方法不同，本文提出了一种类感知的修剪技术来压缩DNNs，为减少DNNs的计算成本提供了一种新的视角。该修剪技术优于以前的修剪解决方案在准确性、修剪比率和FLOPs的减少方面。实验结果证实，这种类感知的修剪技术能够显著减少权重和FLOPs的数量，同时保持高推理准确度。

高效神经网络的类别感知剪枝