Papers › Knowledge Distillation Based on Transformed Teacher Matching
Knowledge Distillation Based on Transformed Teacher Matching
Kaixiang Zheng, En-hui Yang
As a technique to bridge logit matching and probability distribution matching, temperature scaling plays a pivotal role in knowledge distillation (KD). Conventionally, temperature scaling is applied to both teacher's logits and student's logits in KD. Motivated by some recent works, in this paper, we drop instead temperature scaling on the student side, and systematically study the resulting variant of KD, dubbed transformed teacher matching (TTM). By reinterpreting temperature scaling as a power transform of probability distribution, we show that in comparison with the original KD, TTM has an inherent R\'enyi entropy term in its objective function, which serves as an extra regularization term. Extensive experiment results demonstrate that thanks to this inherent regularization, TTM leads to trained students with better generalization than the original KD. To further enhance student's capability to match teacher's power transformed probability distribution, we introduce a sample-adaptive weighting coefficient into TTM, yielding a novel distillation approach dubbed weighted TTM (WTTM). It is shown, by comprehensive experiments, that although WTTM is simple, it is effective, improves upon TTM, and achieves state-of-the-art accuracy performance. Our source code is available at https://github.com/zkxufo/TTM.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Knowledge Distillation | ImageNet | WTTM (T: DeiT III-Small S:DeiT-Tiny) | CRD training setting | ✘ | #23 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | WTTM (T: DeiT III-Small S:DeiT-Tiny) | Top-1 accuracy % | 77.03 | #23 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | WTTM (T:resnet50, S:mobilenet-v1) | Top-1 accuracy % | 73.09 | #26 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | WTTM (T: ResNet-34 S:ResNet-18) | CRD training setting | ✓ | #33 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | WTTM (T: ResNet-34 S:ResNet-18) | Top-1 accuracy % | 72.19 | #33 of 52 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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