Challenges and Limitations of Few-Shot and Zero-Shot Learning

Abstract

Essential to the development of AI and machine learning, this chapter explores the complex areas of few-shot and zero-shot learning. There have been great advancements towards more efficient and adaptive AI systems with few-shot learning and zero-shot learning, respectively, which can learn from minimal data and infer from particular data instances without previous exposure. Nevertheless, there are several limits and difficulties associated with these procedures. This chapter delves deeply into the theoretical foundations of both techniques, explaining how they work and what problems they solve in different ways. It examines the semantic gap, domain adaptation problems, and model bias, as well as the computational restrictions, overfitting, and model generalizability that are intrinsic to few-shot learning and zero-shot learning, respectively. We may better understand the ideas' potential use in different real-world contexts by comparing and contrasting them.