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.

翻译：少样本学习（FSL）是一种新兴的学习范式，试图通过低样本复杂度来学习推理，模仿人类仅从少数示例中进行学习、泛化和外推的方式。尽管FSL试图模仿这些人类特征，但从根本上讲，传统上使用基于情节训练的无元学习方式所定义的FSL任务，实际上并不符合人类获取知识和进行推理的方式。使用情节训练的FSL虽然每个测试类别仅需$K$个实例，但仍需大量来自不同类别的标注训练实例。本文提出了一种新型的约束性少样本学习（CFSL）任务，这是FSL的一种特殊情况，其中每个训练类别的实例数量$M$受到约束，即$M \leq K$，从而在FSL训练和测试过程中施加类似的限制。我们提出了一种基于Cat2Vec的CFSL方法，该方法利用了受认知理论（如模糊痕迹理论和原型理论）启发的新型类别对比损失函数。