Papers › Contrastive Prototypical Network with Wasserstein Confidence Penalty
Contrastive Prototypical Network with Wasserstein Confidence Penalty
Haoqing Wang, Zhi-Hong Deng
Unsupervised few-shot learning aims to learn the inductive bias from unlabeled dataset for solving the novel few-shot tasks. The existing unsupervised few-shot learning models and the contrastive learning models follow a unified paradigm. Therefore, we conduct empirical study under this paradigm and find that pairwise contrast, meta losses and large batch size are the important design factors. This results in our CPN (Contrastive Prototypical Network) model, which combines the prototypical loss with pairwise contrast and outperforms the existing models from this paradigm with modestly large batch size. Furthermore, the one-hot prediction target in CPN could lead to learning the sample-specific information. To this end, we propose Wasserstein Confidence Penalty which can impose appropriate penalty on overconfident predictions based on the semantic relationships among pseudo classes. Our full model, CPNWCP (Contrastive Prototypical Network with Wasserstein Confidence Penalty), achieves state-of-the-art performance on miniImageNet and tieredImageNet under unsupervised setting. Our code is available at https://github.com/Haoqing-Wang/CPNWCP.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | CPNWCP | Accuracy | 53.56 | #11 of 28 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | CPNWCP | Accuracy | 73.21 | #9 of 28 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | CPNWCP | Accuracy | 45.00 | #9 of 12 | Archive leaderboard | report |
| Unsupervised Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | CPNWCP | Accuracy | 62.96 | #9 of 12 | 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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