UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning

• URL: http://arxiv.org/abs/2412.16739v1
• Date: Sat, 21 Dec 2024 19:01:57 GMT
• Title: UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
• Authors: Long Zhou, Fereshteh Shakeri, Aymen Sadraoui, Mounir Kaaniche, Jean-Christophe Pesquet, Ismail Ben Ayed,
• Abstract summary: We advocate and introduce the unrolling paradigm, also referred to as "learning to optimize" Our unrolling approach covers various statistical feature distributions and pre-training paradigms. We report comprehensive experiments, which cover a breadth of fine-grained downstream image classification tasks.
• Score: 35.62208317531141
• License:
• Abstract: 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.

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