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.
翻译:我们考虑对“活性少鞋分类”问题的新提法,其目标是对一个小的、最初没有标签的数据集进行分类,并给它一个非常严格的标签预算。 这个问题可以被视为古典的“传统中性少鞋分类”的对立范例,因为这两种方法都适用于类似的情况。 我们首先提出一种结合统计推论的方法,以及一种与这个框架非常相适应的最初的双层积极学习战略。 然后我们调整了来自TFSC领域的几个标准愿景基准。 我们的实验表明,AFSC的潜在好处是巨大的,平均加权精度可达10%,而同一标签预算的TFSC方法则是最先进的。 我们认为,这一新模式可以导致数据记录学习环境的新发展和标准。