Papers › Few-Shot Learning with Graph Neural Networks
Few-Shot Learning with Graph Neural Networks
Victor Garcia, Joan Bruna
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on 'relational' tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Domain Few-Shot | ChestX | GNN | 5 shot | 25.27 | #6 of 11 | Archive leaderboard | report |
| Cross-Domain Few-Shot | EuroSAT | GNN | 5 shot | 83.64 | #7 of 11 | Archive leaderboard | report |
| Cross-Domain Few-Shot | ISIC2018 | GNN | 5 shot | 43.94 | #10 of 11 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Cars 5-way (1-shot) | GNN++ | Accuracy | 55.85 | #4 of 6 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Cars 5-way (5-shot) | GNN++ | Accuracy | 71.25 | #4 of 6 | Archive leaderboard | report |
| Few-Shot Image Classification | Stanford Dogs 5-way (5-shot) | GNN++ | Accuracy | 62.27 | #4 of 6 | 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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