Summary of Condensed Sample-guided Model Inversion For Knowledge Distillation, by Kuluhan Binici et al.
Condensed Sample-Guided Model Inversion for Knowledge Distillation
by Kuluhan Binici, Shivam Aggarwal, Cihan Acar, Nam Trung Pham, Karianto Leman, Gim Hee Lee, Tulika Mitra
First submitted to arxiv on: 25 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 introduces a method for improving knowledge distillation (KD) performance by utilizing supplementary information from the target dataset. Conventional KD methods rely on synthetic data generated through model inversion to mimic the target data distribution, but this approach does not leverage additional information when it is available. The proposed method uses condensed samples as a form of supplementary information to better approximate the target data distribution, leading to improved KD performance. This approach is versatile and effective across various datasets and model inversion-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes knowledge distillation better by using extra information from the target dataset. Right now, KD relies on fake data made by flipping models upside down, but this doesn’t use real data when it’s available. The new method uses special samples that help make the fake data more realistic, which improves how well the student model learns from the teacher model. This works well with different datasets and ways of making fake data, and can even work with just a few real examples. |
Keywords
» Artificial intelligence » Knowledge distillation » Student model » Synthetic data » Teacher model
