Papers › Information Theoretic Representation Distillation
Information Theoretic Representation Distillation
Roy Miles, Adrian Lopez Rodriguez, Krystian Mikolajczyk
Despite the empirical success of knowledge distillation, current state-of-the-art methods are computationally expensive to train, which makes them difficult to adopt in practice. To address this problem, we introduce two distinct complementary losses inspired by a cheap entropy-like estimator. These losses aim to maximise the correlation and mutual information between the student and teacher representations. Our method incurs significantly less training overheads than other approaches and achieves competitive performance to the state-of-the-art on the knowledge distillation and cross-model transfer tasks. We further demonstrate the effectiveness of our method on a binary distillation task, whereby it leads to a new state-of-the-art for binary quantisation and approaches the performance of a full precision model. Code: www.github.com/roymiles/ITRD
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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 | resnet8x4 (T: resnet32x4 S: resnet8x4) | Top-1 Accuracy (%) | 76.68 | #9 of 27 | Archive leaderboard | report |
| Knowledge Distillation | CIFAR-100 | vgg8 (T:vgg13 S:vgg8) | Top-1 Accuracy (%) | 74.93 | #16 of 27 | Archive leaderboard | report |
| Knowledge Distillation | CIFAR-100 | resnet110 (T:resnet110 S:resnet20) | Top-1 Accuracy (%) | 71.99 | #23 of 27 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | ITRD (T: ResNet-34 S:ResNet-18) | CRD training setting | ✓ | #41 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | ITRD (T: ResNet-34 S:ResNet-18) | Top-1 accuracy % | 71.68 | #41 of 52 | Archive leaderboard | report |
| Knowledge Distillation | ImageNet | ITRD (T: ResNet-34 S:ResNet-18) | model size | 11.69M | #41 of 52 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | BERT - 6 Layers | EM | 81.5 | #50 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | BERT - 6 Layers | F1 | 88.5 | #50 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | BERT - 3 Layers | EM | 77.7 | #97 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | BERT - 3 Layers | F1 | 85.8 | #97 of 213 | 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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