Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification
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Few-shot learning (FSL) is an emergent paradigm of learning that attempts to learn with low sample complexity to mimic the way humans can learn, generalise and extrapolate based on only a few examples. While FSL attempts to mimic these human characteristics, fundamentally, the task of FSL as conventionally described and modelled using meta-learning with episodic-based training does not fully align with how humans acquire and reason with knowledge. FSL with episodic training, while only using K instances of each test class, still requires a large number of labelled instances from disjoint training classes. In this paper, we introduce the novel task of constrained few-shot learning (CFSL), a special case of FSL where the number of training instances of each class is constrained to be less than some value M thus applying a similar restriction during 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.
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