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Generalized Adaptation for Few-Shot Learning

25 Nov 2019arXiv:1911.10807archive 2025-07-28

Liang Song, Jinlu Liu, Yongqiang Qin

Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model. Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model. We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases. Notably, we achieve the accuracy of 87.75% on 5-shot miniImageNet which approximately outperforms existing methods by 10%.

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Tasks

Few-Shot Image ClassificationFew-Shot Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Image Classification CIFAR-FS 5-way (1-shot) ACC + Amphibian Accuracy 73.1 #31 of 38 Archive leaderboard report
Few-Shot Image Classification CIFAR-FS 5-way (5-shot) ACC + Amphibian Accuracy 89.3 #14 of 39 Archive leaderboard report
Few-Shot Image Classification FC100 5-way (1-shot) ACC + Amphibian Accuracy 41.6 #19 of 22 Archive leaderboard report
Few-Shot Image Classification FC100 5-way (5-shot) ACC + Amphibian Accuracy 66.9 #4 of 22 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) ACC + Amphibian Accuracy 62.21 #68 of 105 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) ACC + Amphibian Accuracy 80.75 #50 of 95 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (1-shot) ACC + Amphibian Accuracy 68.77 #38 of 49 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (5-shot) ACC + Amphibian Accuracy 86.75 #23 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.

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