Papers › Understanding the Role of the Projector in Knowledge Distillation
Understanding the Role of the Projector in Knowledge Distillation
Roy Miles, Krystian Mikolajczyk
In this paper we revisit the efficacy of knowledge distillation as a function matching and metric learning problem. In doing so we verify three important design decisions, namely the normalisation, soft maximum function, and projection layers as key ingredients. We theoretically show that the projector implicitly encodes information on past examples, enabling relational gradients for the student. We then show that the normalisation of representations is tightly coupled with the training dynamics of this projector, which can have a large impact on the students performance. Finally, we show that a simple soft maximum function can be used to address any significant capacity gap problems. Experimental results on various benchmark datasets demonstrate that using these insights can lead to superior or comparable performance to state-of-the-art knowledge distillation techniques, despite being much more computationally efficient. In particular, we obtain these results across image classification (CIFAR100 and ImageNet), object detection (COCO2017), and on more difficult distillation objectives, such as training data efficient transformers, whereby we attain a 77.2% top-1 accuracy with DeiT-Ti on ImageNet. Code and models are publicly available.
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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 | CIFAR-100 | SRD (T:resnet-32x4, S:shufflenet-v2) | Top-1 Accuracy (%) | 79.86 | #1 of 27 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T:RegNety 160 S:DeiT-S) | CRD training setting | ✘ | #12 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T:RegNety 160 S:DeiT-S) | Top-1 accuracy % | 82.1 | #12 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T:RegNety 160 S:DeiT-S) | model size | 22M | #12 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T:RegNety 160 S:DeIT-Ti) | CRD training setting | ✘ | #21 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T:RegNety 160 S:DeIT-Ti) | Top-1 accuracy % | 77.2 | #21 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T:RegNety 160 S:DeIT-Ti) | model size | 6M | #21 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T: ResNet-34 S:ResNet-18) | CRD training setting | ✓ | #38 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | SRD (T: ResNet-34 S:ResNet-18) | Top-1 accuracy % | 71.87 | #38 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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