Summary of Decoupling Dark Knowledge Via Block-wise Logit Distillation For Feature-level Alignment, by Chengting Yu et al.
Decoupling Dark Knowledge via Block-wise Logit Distillation for Feature-level Alignment
by Chengting Yu, Fengzhao Zhang, Ruizhe Chen, Aili Wang, Zuozhu Liu, Shurun Tan, Er-Ping Li
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This study reexamines Knowledge Distillation (KD), a method where a larger teacher network guides a smaller student network, with the aim of producing well-performing lightweight models. The paper highlights the potential of the logit-based method and provides a unified perspective on feature alignment to better understand its fundamental distinction from feature-based methods. The authors introduce a block-wise logit distillation framework that applies implicit logit-based feature alignment by gradually replacing teacher’s blocks as intermediate stepping-stone models. The proposed method achieves comparable or superior results to state-of-the-art distillation methods, demonstrating the great potential of combining logit and features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KD helps smaller models learn from larger ones by transferring knowledge via logits or features. Researchers have tried many different approaches, but some recent work has shown that the original logit-based method can still be effective. The key challenge is choosing between using logits or features. This study provides a new way of understanding this choice and proposes a framework that uses both logits and features to achieve good results. |
Keywords
» Artificial intelligence » Alignment » Distillation » Knowledge distillation » Logits
