Papers › UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
Long Zhou, Fereshteh Shakeri, Aymen Sadraoui, Mounir Kaaniche, Jean-Christophe Pesquet, Ismail Ben Ayed
Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistics of the test batches, such as the level of class balance, affecting performances significantly. Such hyper-parameters are empirically grid-searched over validation data, and their configurations may vary substantially with the target dataset and pre-training model, making such empirical searches both sub-optimal and computationally intractable. In this work, we advocate and introduce the unrolling paradigm, also referred to as "learning to optimize", in the context of few-shot learning, thereby learning efficiently and effectively a set of optimized hyper-parameters. Specifically, we unroll a generalization of the ubiquitous Expectation-Maximization (EM) optimizer into a neural network architecture, mapping each of its iterates to a layer and learning a set of key hyper-parameters over validation data. Our unrolling approach covers various statistical feature distributions and pre-training paradigms, including recent foundational vision-language models and standard vision-only classifiers. We report comprehensive experiments, which cover a breadth of fine-grained downstream image classification tasks, showing significant gains brought by the proposed unrolled EM algorithm over iterative variants. The achieved improvements reach up to 10% and 7.5% on vision-only and vision-language benchmarks, respectively.
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 Learning | Mini-ImageNet - 10-Shot Learning | UNEM-Gaussian | Accuracy | 65.7 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Learning | Mini-ImageNet - 20-Shot Learning | UNEM-Gaussian | Accuracy | 73.2 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Learning | Mini-ImageNet - 5-Shot Learning | UNEM-Gaussian | Accuracy | 66.4% | #3 of 3 | Archive leaderboard | report |
| Few-Shot Learning | tieredImageNet - 5-shot | UNEM-Gaussian | Accuracy | 52.3 | #1 of 1 | 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.
Methods
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