论文标题
标签有效元学习的多维信念量化
Multidimensional Belief Quantification for Label-Efficient Meta-Learning
论文作者
论文摘要
基于优化的元学习为几次学习提供了一个有希望的方向，这对于许多现实世界中的计算机视觉应用至关重要。但是，从几个样本中学习会引入不确定性，而对几个关键领域进行量化模型置信度至关重要。此外，元训练中使用的几个射击任务通常是从迭代模型更新的任务分布中随机取样的，从而导致高标签成本和元训练中的计算开销。我们为标签有效元学习的新型不确定性感知的任务选择模型。提出的模型制定了多维信念度量，该度量可以量化已知的不确定性和下限任何给定任务的不确定性。我们的理论结果建立了矛盾的信念与不正确的信念之间的重要关系。理论结果使我们能够估算任务的总不确定性，该任务提供了任务选择的原则标准。进一步开发了一种新型的多Query任务公式，以提高元学习的计算效率和标记效率。在多个现实世界中进行的实验少数图像分类任务证明了所提出的模型的有效性。
Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few samples introduces uncertainty, and quantifying model confidence for few-shot predictions is essential for many critical domains. Furthermore, few-shot tasks used in meta training are usually sampled randomly from a task distribution for an iterative model update, leading to high labeling costs and computational overhead in meta-training. We propose a novel uncertainty-aware task selection model for label efficient meta-learning. The proposed model formulates a multidimensional belief measure, which can quantify the known uncertainty and lower bound the unknown uncertainty of any given task. Our theoretical result establishes an important relationship between the conflicting belief and the incorrect belief. The theoretical result allows us to estimate the total uncertainty of a task, which provides a principled criterion for task selection. A novel multi-query task formulation is further developed to improve both the computational and labeling efficiency of meta-learning. Experiments conducted over multiple real-world few-shot image classification tasks demonstrate the effectiveness of the proposed model.
