Papers › A Baseline for Few-Shot Image Classification
A Baseline for Few-Shot Image Classification
Guneet S. Dhillon, Pratik Chaudhari, Avinash Ravichandran, Stefano Soatto
Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current state-of-the-art on standard datasets such as Mini-ImageNet, Tiered-ImageNet, CIFAR-FS and FC-100 with the same hyper-parameters. The simplicity of this approach enables us to demonstrate the first few-shot learning results on the ImageNet-21k dataset. We find that using a large number of meta-training classes results in high few-shot accuracies even for a large number of few-shot classes. We do not advocate our approach as the solution for few-shot learning, but simply use the results to highlight limitations of current benchmarks and few-shot protocols. We perform extensive studies on benchmark datasets to propose a metric that quantifies the "hardness" of a few-shot episode. This metric can be used to report the performance of few-shot algorithms in a more systematic way.
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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) | Entropy Minimization | 1:1 Accuracy | 67.5 | #7 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet CUB-200 (5-way, 5-shot) | Entropy Minimization | 1:1 Accuracy | 82.9 | #7 of 8 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 1-shot) | Entropy Minimization | 1:1 Accuracy | 58.5 | #9 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Mini-Imagenet (5-way, 5-shot) | Entropy Minimization | 1:1 Accuracy | 74.8 | #7 of 12 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 1-shot) | Entropy Minimization | 1:1 Accuracy | 61.2 | #9 of 9 | Archive leaderboard | report |
| Few-Shot Image Classification | Dirichlet Tiered-Imagenet (5-way, 5-shot) | Entropy Minimization | 1:1 Accuracy | 75.5 | #8 of 9 | 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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