Papers › Prototype Rectification for Few-Shot Learning
Prototype Rectification for Few-Shot Learning
Jinlu Liu, Liang Song, Yongqiang Qin
Few-shot learning requires to recognize novel classes with scarce labeled data. Prototypical network is useful in existing researches, however, training on narrow-size distribution of scarce data usually tends to get biased prototypes. In this paper, we figure out two key influencing factors of the process: the intra-class bias and the cross-class bias. We then propose a simple yet effective approach for prototype rectification in transductive setting. The approach utilizes label propagation to diminish the intra-class bias and feature shifting to diminish the cross-class bias. We also conduct theoretical analysis to derive its rationality as well as the lower bound of the performance. Effectiveness is shown on three few-shot benchmarks. Notably, our approach achieves state-of-the-art performance on both miniImageNet (70.31% on 1-shot and 81.89% on 5-shot) and tieredImageNet (78.74% on 1-shot and 86.92% on 5-shot).
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | Dirichlet CUB-200 (5-way, 1-shot) | BDCSPN | 1:1 Accuracy | 74.5 | #3 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet CUB-200 (5-way, 5-shot) | BDCSPN | 1:1 Accuracy | 87.1 | #6 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 1-shot) | BD-CSPN | 1:1 Accuracy | 67.0 | #3 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 5-shot) | BDCSPN | 1:1 Accuracy | 80.2 | #4 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 1-shot) | BDCSPN | 1:1 Accuracy | 74.1 | #4 of 9 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 5-shot) | BDCSPN | 1:1 Accuracy | 84.8 | #5 of 9 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-ImageNet - 1-Shot Learning | BD-CSPN | Accuracy | 70.31% | #7 of 16 | 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.
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