Meta-Learned Confidence for Few-shot Learning

TL;DR

This paper introduces a meta-learning approach to estimate confidence scores for query samples in few-shot learning, improving transductive inference by adaptively weighting unlabeled data to enhance performance on unseen tasks.

Contribution

It proposes a novel meta-learning method to predict confidence for query samples, enabling more reliable transductive inference in few-shot learning scenarios.

Findings

Achieves state-of-the-art results on four benchmark datasets.

Significantly improves semi-supervised few-shot learning performance.

Outperforms recent strong baselines in various settings.

Abstract

Transductive inference is an effective means of tackling the data deficiency problem in few-shot learning settings. A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples, or confidence-weighted average of all the query samples. However, a caveat here is that the model confidence may be unreliable, which may lead to incorrect predictions. To tackle this issue, we propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries such that they improve the model's transductive inference performance on unseen tasks. We achieve this by meta-learning an input-adaptive distance metric over a task distribution under various model and data perturbations, which will enforce consistency on the model predictions under diverse uncertainties for unseen tasks. Moreover, we additionally suggest a regularization which explicitly enforces the consistency on the predictions across the different dimensions of a high-dimensional embedding vector. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets, on which it largely outperforms strong recent baselines and obtains new state-of-the-art results. Further application on semi-supervised few-shot learning tasks also yields significant performance improvements over the baselines. The source code of our algorithm is available at https://github.com/seongmin-kye/MCT.