Summary of Cnn2gnn: How to Bridge Cnn with Gnn, by Ziheng Jiao and Hongyuan Zhang and Xuelong Li
CNN2GNN: How to Bridge CNN with GNN
by Ziheng Jiao, Hongyuan Zhang, Xuelong Li
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework that bridges the gap between Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). CNNs excel in vision tasks by extracting intra-sample representations, but require extensive training due to stacked convolutional layers. GNNs, on the other hand, successfully explore topological relationships among graph data with few graph neural layers, but cannot be directly applied to non-graph data and have high inference latency. The authors discuss how to combine these complementary strengths and weaknesses by proposing a CNN2GNN framework that distills knowledge from CNNs to GNNs. A differentiable sparse graph learning module is designed to dynamically learn graphs for inductive learning, while response-based distillation transfers knowledge between the two networks. This results in higher performance on Mini-ImageNet compared to traditional CNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper connects two powerful machine learning models: Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at vision tasks, they require lots of training data because of many layers. GNNs are good at understanding relationships between things in graphs, but can’t be used with non-graph data and take a long time to make predictions. The researchers want to find a way to combine these strengths and weaknesses. They propose a new framework called CNN2GNN that helps GNNs learn from CNNs. This makes it possible to use the best of both worlds. |
Keywords
» Artificial intelligence » Distillation » Inference » Machine learning
