Papers › Negative Margin Matters: Understanding Margin in Few-shot Classification
Negative Margin Matters: Understanding Margin in Few-shot Classification
Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, Han Hu
This paper introduces a negative margin loss to metric learning based few-shot learning methods. The negative margin loss significantly outperforms regular softmax loss, and achieves state-of-the-art accuracy on three standard few-shot classification benchmarks with few bells and whistles. These results are contrary to the common practice in the metric learning field, that the margin is zero or positive. To understand why the negative margin loss performs well for the few-shot classification, we analyze the discriminability of learned features w.r.t different margins for training and novel classes, both empirically and theoretically. We find that although negative margin reduces the feature discriminability for training classes, it may also avoid falsely mapping samples of the same novel class to multiple peaks or clusters, and thus benefit the discrimination of novel classes. Code is available at https://github.com/bl0/negative-margin.few-shot.
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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 |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | CUB 200 5-way 1-shot | Neg-Margin | Accuracy | 72.66 | #26 of 36 | Archive leaderboard | report |
| Few-Shot Image Classification | CUB 200 5-way 5-shot | Neg-Margin | Accuracy | 89.40 | #20 of 32 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-ImageNet - 1-Shot Learning | Neg-Margin | Accuracy | 63.85 | #12 of 16 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-ImageNet to CUB - 5 shot learning | Neg-Margin | Accuracy | 69.30 | #2 of 2 | 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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