Relational Knowledge Distillation in 3D Point Clouds (part 1)

1. 1Read the article on Medium to understand the basics of Relational Knowledge Distillation
2. 2Apply Hinton KD for output distribution distillation
3. 3Implement RKD to transfer structural knowledge in 3D point clouds
4. 4Compare the performance of teacher and student models using RKD
5. 5Fine-tune the student model for better results

This article benefits machine learning engineers and researchers working with 3D point clouds, who can apply RKD to improve model performance and efficiency

💡 RKD enables the transfer of structural knowledge, not just individual example outputs, from a teacher model to a student model

💡 Relational Knowledge Distillation (RKD) for 3D point clouds: transfer structural knowledge from teacher to student model