Papers › Geometric Mean Improves Loss For Few-Shot Learning
Geometric Mean Improves Loss For Few-Shot Learning
Tong Wu, Takumi Kobayashi
Few-shot learning (FSL) is a challenging task in machine learning, demanding a model to render discriminative classification by using only a few labeled samples. In the literature of FSL, deep models are trained in a manner of metric learning to provide metric in a feature space which is well generalizable to classify samples of novel classes; in the space, even a few amount of labeled training examples can construct an effective classifier. In this paper, we propose a novel FSL loss based on \emph{geometric mean} to embed discriminative metric into deep features. In contrast to the other losses such as utilizing arithmetic mean in softmax-based formulation, the proposed method leverages geometric mean to aggregate pair-wise relationships among samples for enhancing discriminative metric across class categories. The proposed loss is not only formulated in a simple form but also is thoroughly analyzed in theoretical ways to reveal its favorable characteristics which are favorable for learning feature metric in FSL. In the experiments on few-shot image classification tasks, the method produces competitive performance in comparison to the other losses.
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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 | CIFAR-FS 5-way (1-shot) | GML (ResNet-12) | Accuracy | 71.09 | #33 of 38 | Archive leaderboard | report |
| Few-Shot Image Classification | CIFAR-FS 5-way (5-shot) | GML (ResNet-12) | Accuracy | 85.08 | #31 of 39 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | GML (ResNet-12) | Accuracy | 65.51 | #55 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | GML (ResNet-12) | Accuracy | 81.13 | #47 of 95 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | GML (ResNet-12) | Accuracy | 69.61 | #33 of 49 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | GML (ResNet-12) | Accuracy | 84.04 | #34 of 51 | 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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