Papers › Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Jiechao Guan, Zhiwu Lu, Tao Xiang, Ji-Rong Wen
To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes. A major challenge for FSL is that the distribution of the unseen classes is different from that of those seen, resulting in poor generalization even when a model is meta-trained on the seen classes. This class-difference-caused distribution shift can be considered as a special case of domain shift. In this paper, for the first time, we propose a domain adaptation prototypical network with attention (DAPNA) to explicitly tackle such a domain shift problem in a meta-learning framework. Specifically, armed with a set transformer based attention module, we construct each episode with two sub-episodes without class overlap on the seen classes to simulate the domain shift between the seen and unseen classes. To align the feature distributions of the two sub-episodes with limited training samples, a feature transfer network is employed together with a margin disparity discrepancy (MDD) loss. Importantly, theoretical analysis is provided to give the learning bound of our DAPNA. Extensive experiments show that our DAPNA outperforms the state-of-the-art FSL alternatives, often by significant margins.
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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 | Mini-ImageNet-CUB 5-way (1-shot) | DAPNA | Accuracy | 49.44 | #4 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-ImageNet-CUB 5-way (5-shot) | DAPNA | Accuracy | 68.33 | #4 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | DAPNA | Accuracy | 71.88 | #29 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | DAPNA | Accuracy | 84.07 | #33 of 95 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | DAPNA | Accuracy | 69.14 | #35 of 49 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | DAPNA | Accuracy | 85.82 | #30 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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