论文标题
限制的很少的学习：类似人类的低样本复杂性学习和非剧本文本分类
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification
论文作者
论文摘要
很少有学习的学习（FSL）是一种新兴的学习范式，它试图学习以低样本的复杂性来模仿人类仅从少数看到的例子中学习，概括和推断的方式。尽管FSL试图模仿这些人类特征，但从根本上讲，FSL的任务通常使用基于情节的训练进行元学习制定的FSL任务并不与人类的获取方式和与知识相吻合。 FSL进行了情节培训，虽然仅需要每个测试课程的$ K $实例，但仍需要大量的脱节课程标记的培训实例。在本文中，我们介绍了限制了少量学习的新任务（CFSL），这是FSL的特殊情况，其中$ m $，每个培训类的实例数受到限制，因此$ M \ leq k $因此在FSL培训和测试中应用了类似的限制。我们提出了一种使用一种受认知理论（例如模糊痕量理论和原型理论）启发的新型分类对比损失来利用CFSL利用CFSL的方法。
Few-shot learning (FSL) is an emergent paradigm of learning that attempts to learn to reason with low sample complexity to mimic the way humans learn, generalise and extrapolate from only a few seen examples. While FSL attempts to mimic these human characteristics, fundamentally, the task of FSL as conventionally formulated using meta-learning with episodic-based training does not in actuality align with how humans acquire and reason with knowledge. FSL with episodic training, while only requires $K$ instances of each test class, still requires a large number of labelled training instances from disjoint classes. In this paper, we introduce the novel task of constrained few-shot learning (CFSL), a special case of FSL where $M$, the number of instances of each training class is constrained such that $M \leq K$ thus applying a similar restriction during FSL training and test. We propose a method for CFSL leveraging Cat2Vec using a novel categorical contrastive loss inspired by cognitive theories such as fuzzy trace theory and prototype theory.
