CNN2GNN: How to Bridge CNN With GNN.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Convolutional Neural Networks (CNNs) excel in vision tasks by extracting intra-sample representations but require significant training resources.
• Graph Neural Networks (GNNs) effectively model topological relationships in graph data with fewer layers but are limited to graph-structured data.
• Bridging the gap between CNNs and GNNs is crucial for leveraging their complementary strengths in diverse machine learning scenarios.

Purpose of the Study:

• To propose a novel framework, CNN2GNN, that unifies CNNs and GNNs through knowledge distillation.
• To enable GNNs to process non-graph data by dynamically learning graph structures.
• To enhance the efficiency and performance of deep learning models by combining intra-sample and topological feature extraction.

Main Methods:

• Designed a differentiable sparse graph learning module to dynamically learn graph structures for inductive learning.
• Implemented a response-based distillation method to transfer knowledge from CNNs to GNNs.
• Unified CNN and GNN architectures into a cohesive framework (CNN2GNN).

Main Results:

• The proposed CNN2GNN framework successfully bridges heterogeneous networks (CNNs and GNNs).
• The distilled GNN model demonstrated significantly higher performance on the Mini-ImageNet dataset compared to deep CNNs like ResNet152.
• Achieved superior results with a two-layer GNN, highlighting the efficiency of the knowledge distillation process.

Conclusions:

• CNN2GNN effectively unifies CNNs and GNNs, overcoming the limitations of each.
• The framework offers a more efficient and performant approach to vision tasks by integrating intra-sample and topological learning.
• This research paves the way for more powerful and versatile deep learning models by combining diverse network architectures.