Papers › A Closer Look at Few-shot Classification
A Closer Look at Few-shot Classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, Jia-Bin Huang
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this paper, we present 1) a consistent comparative analysis of several representative few-shot classification algorithms, with results showing that deeper backbones significantly reduce the performance differences among methods on datasets with limited domain differences, 2) a modified baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the \miniI and the CUB datasets, and 3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classification algorithms. Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones. In a realistic cross-domain evaluation setting, we show that a baseline method with a standard fine-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.
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Code
Syntology Ran 7 of 8 code samples harvested from 4 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 4 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
8 samples harvested; 7 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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) | Baseline++ | 1:1 Accuracy | 69.4 | #6 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet CUB-200 (5-way, 5-shot) | Baseline++ | 1:1 Accuracy | 87.5 | #5 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 1-shot) | Baseline ++ | 1:1 Accuracy | 60.4 | #7 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 5-shot) | Baseline++ | 1:1 Accuracy | 79.7 | #6 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 1-shot) | Baseline++ | 1:1 Accuracy | 68.0 | #7 of 9 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 5-shot) | Baseline++ | 1:1 Accuracy | 84.2 | #7 of 9 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-ImageNet-CUB 5-way (1-shot) | Baseline++ (Chen et al., 2019) | Accuracy | 33.04 | #12 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-ImageNet-CUB 5-way (5-shot) | Baseline++ (Chen et al., 2019) | Accuracy | 62.04 | #6 of 8 | 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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