Papers › Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification
Nikita Dvornik, Cordelia Schmid, Julien Mairal
Popular approaches for few-shot classification consist of first learning a generic data representation based on a large annotated dataset, before adapting the representation to new classes given only a few labeled samples. In this work, we propose a new strategy based on feature selection, which is both simpler and more effective than previous feature adaptation approaches. First, we obtain a multi-domain representation by training a set of semantically different feature extractors. Then, given a few-shot learning task, we use our multi-domain feature bank to automatically select the most relevant representations. We show that a simple non-parametric classifier built on top of such features produces high accuracy and generalizes to domains never seen during training, which leads to state-of-the-art results on MetaDataset and improved accuracy on mini-ImageNet.
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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 | Meta-Dataset | SUR | Accuracy | 70.72 | #10 of 22 | Archive leaderboard | report |
| Few-Shot Image Classification | Meta-Dataset | SUR-pnf | Accuracy | 69.3 | #13 of 22 | Archive leaderboard | report |
| Few-Shot Image Classification | Meta-Dataset Rank | SUR | Mean Rank | 4.2 | #4 of 13 | Archive leaderboard | report |
| Few-Shot Image Classification | Meta-Dataset Rank | SUR-pnf | Mean Rank | 4.25 | #5 of 13 | 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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