论文标题
活跃的几杆分类：用于数据筛查学习设置的新范式
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings
论文作者
论文摘要
我们考虑了一种新颖的表述，即主动射击分类（AFSC）的问题，其目的是对标签预算非常约束的小型数据集进行分类。这个问题可以看作是与经典的跨传输差异分类（TFSC）的竞争对手范式，因为这两种方法都适用于相似的条件。我们首先提出了一种结合统计推断的方法，以及一种非常适合该框架的原始两级积极学习策略。然后，我们从TFSC领域调整了几个标准视觉基准。我们的实验表明，AFSC的潜在优势可能是很大的，与最先进的TFSC方法相比，对于同一标签预算，平均加权准确度高达10％。我们认为，这种新的范式可能会导致数据筛选学习设置的新发展和标准。
We consider a novel formulation of the problem of Active Few-Shot Classification (AFSC) where the objective is to classify a small, initially unlabeled, dataset given a very restrained labeling budget. This problem can be seen as a rival paradigm to classical Transductive Few-Shot Classification (TFSC), as both these approaches are applicable in similar conditions. We first propose a methodology that combines statistical inference, and an original two-tier active learning strategy that fits well into this framework. We then adapt several standard vision benchmarks from the field of TFSC. Our experiments show the potential benefits of AFSC can be substantial, with gains in average weighted accuracy of up to 10% compared to state-of-the-art TFSC methods for the same labeling budget. We believe this new paradigm could lead to new developments and standards in data-scarce learning settings.
