Relational Knowledge Distillation

Relational knowledge distillation (RKD) focuses on transferring not only individual feature representations but also the relationships between them from a larger "teacher" model to a smaller, more efficient "student" model. Current research emphasizes leveraging graph convolutional networks and transformers to capture these relationships, particularly in challenging domains like low-resolution image recognition, 3D point cloud processing, and cross-domain adaptation. This technique improves model efficiency and robustness, particularly in scenarios with limited data or computational resources, impacting various fields including computer vision, autonomous driving, and knowledge graph integration.