Papers › SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, Laurens van der Maaten
Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features and perform classification using a nearest-neighbor classifier. This paper studies the accuracy of nearest-neighbor baselines without meta-learning. Surprisingly, we find simple feature transformations suffice to obtain competitive few-shot learning accuracies. For example, we find that a nearest-neighbor classifier used in combination with mean-subtraction and L2-normalization outperforms prior results in three out of five settings on the miniImageNet dataset.
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Code
Syntology Ran 3 of 9 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 3 ran · our draft was wrong.
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Code Syntology ran Syntology
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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) | Simpleshot | 1:1 Accuracy | 70.6 | #5 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet CUB-200 (5-way, 5-shot) | Simpleshot | 1:1 Accuracy | 87.5 | #4 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 1-shot) | Simpleshot | 1:1 Accuracy | 63.0 | #5 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 5-shot) | Simpleshot | 1:1 Accuracy | 80.1 | #5 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 1-shot) | Simpleshot | 1:1 Accuracy | 69.6 | #6 of 9 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 5-shot) | Simpleshot | 1:1 Accuracy | 84.7 | #6 of 9 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | SimpleShot (CL2N-DenseNet) | Accuracy | 64.29 | #61 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | SimpleShot (CL2N-DenseNet) | Accuracy | 81.5 | #46 of 95 | 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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