Papers › Wasserstein Contrastive Representation Distillation
Wasserstein Contrastive Representation Distillation
Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture important structural knowledge in the teacher network and often lacks the ability for feature generalization, particularly in situations when teacher and student are built to address different classification tasks. We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. The primal form is used for local contrastive knowledge transfer within a mini-batch, effectively matching the distributions of features between the teacher and the student networks. Experiments demonstrate that the proposed WCoRD method outperforms state-of-the-art approaches on privileged information distillation, model compression and cross-modal transfer.
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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 (%) | 76.15 | #12 of 27 | Archive leaderboard | report |
| Knowledge Distillation | CIFAR-100 | vgg8 (T:vgg13 S:vgg8) | Top-1 Accuracy (%) | 74.72 | #18 of 27 | Archive leaderboard | report |
| Knowledge Distillation | CIFAR-100 | resnet110 (T:resnet110 S:resnet20) | Top-1 Accuracy (%) | 71.88 | #24 of 27 | 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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