Summary of Quantifying Knowledge Distillation Using Partial Information Decomposition, by Pasan Dissanayake et al.
Quantifying Knowledge Distillation Using Partial Information Decomposition
by Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Qiuyi Zhang, Sanghamitra Dutta
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 A novel machine learning study investigates the limits of knowledge transfer through model distillation, seeking to understand how much information from a larger teacher model can be effectively transferred to a smaller student model. By applying Partial Information Decomposition (PID) techniques, researchers aim to quantify the quality and relevance of the transferred knowledge, enabling more effective deployment of complex models in resource-constrained environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores the limits of knowledge transfer through distillation, asking what information from a teacher model can be effectively passed on to a student model. By using Partial Information Decomposition (PID) techniques, researchers can measure how much useful information is being transferred and what’s getting lost in translation. This helps us better understand how to deploy complex models in places with limited resources. |
Keywords
» Artificial intelligence » Distillation » Machine learning » Student model » Teacher model » Translation
