论文标题
古典序列匹配是竞争激烈的几级单级学习者
Classical Sequence Match is a Competitive Few-Shot One-Class Learner
论文作者
论文摘要
如今，基于变压器的模型逐渐成为人工智能先驱的默认选择。即使在几个镜头的情况下，这些模型也会显示出优势。在本文中，我们重新审视了经典方法，并提出了一种新的几次替代方法。具体而言，我们研究了几个射门的单级问题，该问题实际上以已知样本为参考来检测未知实例是否属于同一类。可以从序列匹配的角度研究此问题。结果表明，使用元学习，经典序列匹配方法，即比较聚集物，显着优于变形金刚。经典方法所需的培训成本要少得多。此外，我们在简单的微调和元学习下进行两种序列匹配方法之间进行了经验比较。元学习导致变压器模型的特征具有高相关维度。原因与变压器模型的层和头部数量密切相关。实验代码和数据可从https://github.com/hmt2014/fewone获得
Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers. The models also show superiority even in the few-shot scenarios. In this paper, we revisit the classical methods and propose a new few-shot alternative. Specifically, we investigate the few-shot one-class problem, which actually takes a known sample as a reference to detect whether an unknown instance belongs to the same class. This problem can be studied from the perspective of sequence match. It is shown that with meta-learning, the classical sequence match method, i.e. Compare-Aggregate, significantly outperforms transformer ones. The classical approach requires much less training cost. Furthermore, we perform an empirical comparison between two kinds of sequence match approaches under simple fine-tuning and meta-learning. Meta-learning causes the transformer models' features to have high-correlation dimensions. The reason is closely related to the number of layers and heads of transformer models. Experimental codes and data are available at https://github.com/hmt2014/FewOne
