Papers › Universal Representation Learning from Multiple Domains for Few-shot Classification
Universal Representation Learning from Multiple Domains for Few-shot Classification
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods use adaptation networks for aligning their features to new domains or select the relevant features from multiple domain-specific feature extractors. In this work, we propose to learn a single set of universal deep representations by distilling knowledge of multiple separately trained networks after co-aligning their features with the help of adapters and centered kernel alignment. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step in a similar spirit to distance learning methods. We rigorously evaluate our model in the recent Meta-Dataset benchmark and demonstrate that it significantly outperforms the previous methods while being more efficient. Our code will be available at https://github.com/VICO-UoE/URL.
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Code
Syntology Ran 12 of 26 code samples harvested from 4 repositories linked to this paper; 14 have no recorded run. Of those that ran: 11 ran · our draft was wrong; 1 ran with no contract checked.
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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 | Meta-Dataset | URL (ResNet18, 84x84 image, shuffled data, scratch, MDL) | Accuracy | 75.75 | #6 of 22 | 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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