Summary of Adaptive Explicit Knowledge Transfer For Knowledge Distillation, by Hyungkeun Park et al.
Adaptive Explicit Knowledge Transfer for Knowledge Distillation
by Hyungkeun Park, Jong-Seok Lee
First submitted to arxiv on: 3 Sep 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 new approach to logit-based knowledge distillation (KD) for classification tasks. The authors aim to improve the efficiency of logit-based KD by effectively delivering the probability distribution for non-target classes from the teacher model to the student model. They show that this implicit knowledge has an adaptive effect on learning and propose a new loss function that enables the student to learn both explicit and implicit knowledge in an adaptive manner. The authors also separate classification and distillation tasks for effective distillation and inter-class relationship modeling. Experimental results demonstrate improved performance compared to state-of-the-art KD methods on CIFAR-100 and ImageNet datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about improving how computers can learn from each other. It’s like teaching a student what they need to know without telling them directly. The authors found that by sharing more information, the student can learn better. They developed a new way of doing this that helps the student learn not just what it needs to know but also how confident the teacher is in their answer. This approach worked well on two big datasets and could be useful for many other applications. |
Keywords
» Artificial intelligence » Classification » Distillation » Knowledge distillation » Loss function » Probability » Student model » Teacher model
