Summary of Dynamic Temperature Knowledge Distillation, by Yukang Wei et al.
Dynamic Temperature Knowledge Distillation
by Yukang Wei, Yu Bai
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes a novel approach to knowledge distillation (KD) called Dynamic Temperature Knowledge Distillation (DTKD), which introduces a dynamic temperature control for both teacher and student models simultaneously. The authors argue that traditional approaches often overlook the complexities of samples with varying levels of difficulty and neglect the distinct capabilities of different teacher-student pairings, leading to less-than-ideal transfer of knowledge. To address this issue, DTKD uses a “sharpness” metric to quantify the smoothness of a model’s output distribution and derives sample-specific temperatures for each model. The authors demonstrate that DTKD performs comparably to leading KD techniques on CIFAR-100 and ImageNet-2012 datasets with added robustness in Target Class KD and None-target Class KD. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to learn from other models, called knowledge distillation. Right now, we use a fixed temperature when teaching one model to another, which isn’t very good because it doesn’t take into account how hard or easy certain things are to learn. The new approach, called DTKD, tries to fix this by making the temperature change depending on what’s being learned. This helps models learn better and more robustly from each other. |
Keywords
* Artificial intelligence * Knowledge distillation * Temperature
