Few-Shot and Zero-Shot Learning: Pushing the Boundaries of Data-Efficient AI

Abstract

The rapid expansion of artificial intelligence (AI) across diverse fields has traditionally relied on the availability of large labeled datasets to train effective models. However, the collection and labeling of massive datasets are expensive, time-consuming, and often impractical in specialized domains. Few-shot and zero-shot learning have emerged as groundbreaking approaches to overcome the dependency on large datasets by enabling models to generalize from minimal or no examples. Few-shot learning focuses on training models that can learn new tasks with just a handful of examples, while zero-shot learning enables models to infer and perform tasks they have never encountered before by leveraging external knowledge or semantic information. These data-efficient paradigms are reshaping the future of AI by enhancing its adaptability, scalability, and deployment across low-resource settings. This paper explores the foundations, methodologies, applications, challenges, and future directions of few-shot and zero-shot learning, emphasizing their critical role in the evolution of intelligent, versatile systems.