提出粗到细的图卷积网络剪枝方法，兼顾高效与精度。
Learning Coarse-to-Fine Pruning of Graph Convolutional Networks for Skeleton-based Recognition
- 用四重参数化实现粗粒度到细粒度的渐进式剪枝
- 在SBU和FPHA数据集上优于现有基线方法
- 适合需要高效率且保持精度的骨架识别任务
幅度剪枝是轻量级网络设计中的常用方法，通过移除权重值最小的连接来实现压缩。该方法可分为结构化和非结构化两种：前者效率高但精度低，后者灵活性强且精度好，但计算性能差。本文提出一种新型粗到细（CTF）剪枝方法，结合两者优势并克服其不足。该方法采用新颖的CTF参数化，将每条连接的掩码表示为四个参数化的哈达玛积，分别捕获通道、列、行和逐元素级别的剪枝。细粒度剪枝仅在粗粒度剪枝关闭时启用，从而在保持高效率的同时提升模型表现。在骨架识别这一挑战性任务上，基于标准SBU和FPHA数据集的大量实验表明，所提方法显著优于多种基线及相关工作。
原文摘要 · Abstract (English)
Magnitude Pruning is a staple lightweight network design method which seeks to remove connections with the smallest magnitude. This process is either achieved in a structured or unstructured manner. While structured pruning allows reaching high efficiency, unstructured one is more flexible and leads to better accuracy, but this is achieved at the expense of low computational performance. In this paper, we devise a novel coarse-to-fine (CTF) method that gathers the advantages of structured and unstructured pruning while discarding their inconveniences to some extent. Our method relies on a novel CTF parametrization that models the mask of each connection as the Hadamard product involving four parametrizations which capture channel-wise, column-wise, row-wise and entry-wise pruning respectively. Hence, fine-grained pruning is enabled only when the coarse-grained one is disabled, and this leads to highly efficient networks while being effective. Extensive experiments conducted on the challenging task of skeleton-based recognition, using the standard SBU and FPHA datasets, show the clear advantage of our CTF approach against different baselines as well as the related work.
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