Papers › Knowledge Distillation from A Stronger Teacher
Knowledge Distillation from A Stronger Teacher
Tao Huang, Shan You, Fei Wang, Chen Qian, Chang Xu
Unlike existing knowledge distillation methods focus on the baseline settings, where the teacher models and training strategies are not that strong and competing as state-of-the-art approaches, this paper presents a method dubbed DIST to distill better from a stronger teacher. We empirically find that the discrepancy of predictions between the student and a stronger teacher may tend to be fairly severer. As a result, the exact match of predictions in KL divergence would disturb the training and make existing methods perform poorly. In this paper, we show that simply preserving the relations between the predictions of teacher and student would suffice, and propose a correlation-based loss to capture the intrinsic inter-class relations from the teacher explicitly. Besides, considering that different instances have different semantic similarities to each class, we also extend this relational match to the intra-class level. Our method is simple yet practical, and extensive experiments demonstrate that it adapts well to various architectures, model sizes and training strategies, and can achieve state-of-the-art performance consistently on image classification, object detection, and semantic segmentation tasks. Code is available at: https://github.com/hunto/DIST_KD .
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Code
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Code Syntology ran Syntology
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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 | resnet8x4 (T: resnet32x4 S: resnet8x4) | Top-1 Accuracy (%) | 76.31 | #10 of 27 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | DIST (T: Swin-L S: Swin-T) | CRD training setting | ✘ | #10 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | DIST (T: Swin-L S: Swin-T) | Top-1 accuracy % | 82.3 | #10 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | DIST (T: Swin-L S: Swin-T) | model size | 29M | #10 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | DIST (T: ResNet-34 S:ResNet-18) | CRD training setting | ✘ | #34 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | DIST (T: ResNet-34 S:ResNet-18) | Top-1 accuracy % | 72.07 | #34 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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