论文标题
训练带有相位模型的轻量级图形卷积网络
Training Lightweight Graph Convolutional Networks with Phase-field Models
论文作者
论文摘要
在本文中，我们使用特定类别的正规化器（被称为相磁模型（PFM））设计了轻量级图形卷积网络（GCN）。 PFMS使用特定的超本地术语表现出双相行为，该术语允许训练GCN的拓扑和权重参数，作为单个“端到端”优化问题的一部分。我们提出的解决方案还依赖于修复化，将拓扑的面具推向了二进制值，从而导致有效的拓扑选择和高概括，同时实施任何有针对性的修剪速率。口罩和权重共享相同的潜在变量，这进一步增强了所得轻质GCN的概括能力。对基于骨架的识别的挑战性任务进行的广泛实验表明，PFMS对其他主食正规机以及相关的轻量级设计方法的表现超出。
In this paper, we design lightweight graph convolutional networks (GCNs) using a particular class of regularizers, dubbed as phase-field models (PFMs). PFMs exhibit a bi-phase behavior using a particular ultra-local term that allows training both the topology and the weight parameters of GCNs as a part of a single "end-to-end" optimization problem. Our proposed solution also relies on a reparametrization that pushes the mask of the topology towards binary values leading to effective topology selection and high generalization while implementing any targeted pruning rate. Both masks and weights share the same set of latent variables and this further enhances the generalization power of the resulting lightweight GCNs. Extensive experiments conducted on the challenging task of skeleton-based recognition show the outperformance of PFMs against other staple regularizers as well as related lightweight design methods.
