Structure pruning is an effective method to compress and accelerate neural
networks. While filter and channel pruning are preferable to other structure
pruning methods in terms of realistic acceleration and hardware compatibility,
pruning methods with a finer granularity, such as intra-channel pruning, are
expected to be capable of yielding more compact and computationally efficient
networks. Typical intra-channel pruning methods utilize a static and
hand-crafted pruning granularity due to a large search space, which leaves room
for improvement in their pruning performance. In this work, we introduce a
novel structure pruning method, termed as dynamic structure pruning, to
identify optimal pruning granularities for intra-channel pruning. In contrast
to existing intra-channel pruning methods, the proposed method automatically
optimizes dynamic pruning granularities in each layer while training deep
neural networks. To achieve this, we propose a differentiable group learning
method designed to efficiently learn a pruning granularity based on
gradient-based learning of filter groups. The experimental results show that
dynamic structure pruning achieves state-of-the-art pruning performance and
better realistic acceleration on a GPU compared with channel pruning. In
particular, it reduces the FLOPs of ResNet50 by 71.85% without accuracy
degradation on the ImageNet dataset. Our code is available at
this https URL.

本文提出了一种名为动态结构修剪的结构修剪方法，基于组梯度学习实现了细粒度修剪，并在训练深度神经网络时优化修剪粒度，实验结果表明在 ImageNet 数据集上，动态结构修剪对 ResNet50 模型进行修剪可将 FLOPs 降低 71.85% 且不影响模型准确率，并在 GPU 加速方面优于通道修剪。