用图神经网络分析网络结构，自动剪枝更精准
Structure-Aware Automatic Channel Pruning by Searching with Graph Embedding
- 用图卷积网络建模网络结构关系，捕捉全局依赖
- 在ImageNet上压缩率更高，准确率仍保持竞争力
- 无需人工干预，可自动搜索最优剪枝组合
通道剪枝是降低深度神经网络计算开销的重要技术，有助于在资源受限设备上高效部署。然而，现有方法多依赖局部启发式或基于权重的准则，难以捕捉网络中的全局结构依赖，导致剪枝决策次优且模型性能下降。为此，我们提出一种新颖的结构感知自动通道剪枝框架（SACP），利用图卷积网络（GCNs）建模网络拓扑，学习各通道的全局重要性。通过编码网络内部的结构关系，实现拓扑感知的剪枝，整个过程完全自动化，减少人工干预。我们限定剪枝率组合在特定空间内，该空间可动态调整，并采用搜索策略确定最优剪枝率组合。在多个基准数据集（CIFAR-10、ImageNet）和多种模型（ResNet、VGG16）上的大量实验表明，SACP在压缩效率上优于当前最优剪枝方法，且在准确率保留方面具有竞争力。
原文摘要 · Abstract (English)
Channel pruning is a powerful technique to reduce the computational overhead of deep neural networks, enabling efficient deployment on resource-constrained devices. However, existing pruning methods often rely on local heuristics or weight-based criteria that fail to capture global structural dependencies within the network, leading to suboptimal pruning decisions and degraded model performance. To address these limitations, we propose a novel structure-aware automatic channel pruning (SACP) framework that utilizes graph convolutional networks (GCNs) to model the network topology and learn the global importance of each channel. By encoding structural relationships within the network, our approach implements topology-aware pruning and this pruning is fully automated, reducing the need for human intervention. We restrict the pruning rate combinations to a specific space, where the number of combinations can be dynamically adjusted, and use a search-based approach to determine the optimal pruning rate combinations. Extensive experiments on benchmark datasets (CIFAR-10, ImageNet) with various models (ResNet, VGG16) demonstrate that SACP outperforms state-of-the-art pruning methods on compression efficiency and competitive on accuracy retention.
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