Summary of Relational Representation Distillation, by Nikolaos Giakoumoglou et al.
Relational Representation Distillation
by Nikolaos Giakoumoglou, Tania Stathaki
First submitted to arxiv on: 16 Jul 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 approach to knowledge distillation (KD) called Relational Representation Distillation (RRD), which improves the transfer of complex knowledge from a large teacher model to a smaller student model while maintaining computational efficiency. RRD employs sharpened distributions of pairwise similarities among different instances as a relation metric, matching feature embeddings between student and teacher models. This method demonstrates superior performance on CIFAR-100 and ImageNet ILSVRC-2012, outperforming traditional KD and sometimes even the teacher network when combined with KD. It also transfers successfully to other datasets like Tiny ImageNet and STL-10. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of sharing knowledge from a big model to a smaller one is being explored. This method, called Relational Representation Distillation (RRD), helps make sure complex ideas are transferred correctly while keeping the smaller model efficient. RRD looks at how similar different instances are and uses that information to match the ideas in the teacher and student models. This approach works better than usual on some big datasets and even beats the original model sometimes. |
Keywords
* Artificial intelligence * Distillation * Knowledge distillation * Student model * Teacher model
