Summary of Scale Decoupled Distillation, by Shicai Wei Chunbo Luo Yang Luo
Scale Decoupled Distillation
by Shicai Wei Chunbo Luo Yang Luo
First submitted to arxiv on: 20 Mar 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 proposed Scale Decoupled Distillation (SDD) method tackles the limitations of existing logit-based knowledge distillation approaches. By decoupling the global logit output into multiple local outputs, SDD establishes separate distillation pipelines for each, allowing students to inherit fine-grained and unambiguous logit knowledge. This novel technique also enables the transfer of consistent and complementary logit knowledge, guiding students to focus on ambiguous samples and improve their discrimination ability. Empirical evaluations on various benchmark datasets demonstrate SDD’s effectiveness across diverse teacher-student pairs, particularly in fine-grained classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to teach machines using something called “logit knowledge distillation.” Right now, this method isn’t working as well as it could because it tries to transfer too much information at once. The researchers developed a simple solution called Scale Decoupled Distillation (SDD). SDD breaks down the information into smaller pieces and helps students learn more accurately by focusing on specific details. |
Keywords
» Artificial intelligence » Classification » Distillation » Knowledge distillation
