Distilling Knowledge from Graph Convolutional Networks
arXiv:2003.10477
Abstract
Existing knowledge distillation methods focus on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph convolutional networks (GCN) that handle non-grid data. In this paper, we propose to our best knowledge the first dedicated approach to distilling knowledge from a pre-trained GCN model. To enable the knowledge transfer from the teacher GCN to the student, we propose a local structure preserving module that explicitly accounts for the topological semantics of the teacher. In this module, the local structure information from both the teacher and the student are extracted as distributions, and hence minimizing the distance between these distributions enables topology-aware knowledge transfer from the teacher, yielding a compact yet high-performance student model. Moreover, the proposed approach is readily extendable to dynamic graph models, where the input graphs for the teacher and the student may differ. We evaluate the proposed method on two different datasets using GCN models of different architectures, and demonstrate that our method achieves the state-of-the-art knowledge distillation performance for GCN models. Code is publicly available at https://github.com/ihollywhy/DistillGCN.PyTorch.
Accepted by CVPR 2020
References in corpus (19)
- Distilling the Knowledge in a Neural Network
- Semi-Supervised Classification with Graph Convolutional Networks
- Inductive Representation Learning on Large Graphs
- Neural Message Passing for Quantum Chemistry
- FitNets: Hints for Thin Deep Nets
- Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
- Do Deep Nets Really Need to be Deep?
- Predicting multicellular function through multi-layer tissue networks
- Gated Graph Sequence Neural Networks
- Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
- Dynamic Graph CNN for Learning on Point Clouds
- FPNN: Field Probing Neural Networks for 3D Data
- Topology Adaptive Graph Convolutional Networks
- Adaptive Sampling Towards Fast Graph Representation Learning
- Net2Net: Accelerating Learning via Knowledge Transfer
- Multi-Task Zipping via Layer-wise Neuron Sharing
- Binarized Collaborative Filtering with Distilling Graph Convolutional Networks
- Knowledge Flow: Improve Upon Your Teachers
- Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning