Papers › MetaFun: Meta-Learning with Iterative Functional Updates
MetaFun: Meta-Learning with Iterative Functional Updates
Jin Xu, Jean-Francois Ton, Hyunjik Kim, Adam R. Kosiorek, Yee Whye Teh
We develop a functional encoder-decoder approach to supervised meta-learning, where labeled data is encoded into an infinite-dimensional functional representation rather than a finite-dimensional one. Furthermore, rather than directly producing the representation, we learn a neural update rule resembling functional gradient descent which iteratively improves the representation. The final representation is used to condition the decoder to make predictions on unlabeled data. Our approach is the first to demonstrates the success of encoder-decoder style meta-learning methods like conditional neural processes on large-scale few-shot classification benchmarks such as miniImageNet and tieredImageNet, where it achieves state-of-the-art performance.
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Code
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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 | Mini-Imagenet 5-way (1-shot) | MetaFun-Attention | Accuracy | 64.13 | #62 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | Mini-Imagenet 5-way (5-shot) | MetaFun-Attention | Accuracy | 80.82 | #48 of 95 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (1-shot) | MetaFun-Attention | Accuracy | 67.72 | #40 of 49 | Archive leaderboard | report |
| Few-Shot Image Classification | Tiered ImageNet 5-way (5-shot) | MetaFun-Kernel | Accuracy | 83.28 | #37 of 51 | 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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