Zero-Shot Learning and Few-Shot Learning with Generative AI: Bridging the Data Gap for Real-World Applications

Abstract

Modern artificial intelligence systems frequently rely on vast amounts of labeled data to achieve robust performance, yet many real-world scenarios suffer from limited data availability. This paper investigates the potential of integrating zero-shot and few-shot learning paradigms with generative AI models to bridge the persistent data gap. Zero-shot learning empowers models to recognize and classify instances from unseen categories by leveraging semantic descriptors, while few-shot learning focuses on adapting models to new classes using only a handful of examples. Generative AI techniques, such as advanced generative adversarial networks and transformer-based models, can synthesize realistic data samples that mimic complex distributions found in natural environments. By combining these approaches, our methodology offers a dual advantage: it not only enhances model generalization across diverse tasks but also mitigates the challenges posed by data scarcity. We demonstrate the effectiveness of this hybrid framework through experiments in domains including computer vision, natural language processing, and anomaly detection, where traditional data collection is prohibitive. Our analysis reveals that the strategic use of generated data significantly boosts learning outcomes, even when initial training samples are sparse. Furthermore, the adaptability of the proposed system makes it suitable for dynamic, real-world applications where new categories continuously emerge. Overall, this study provides a comprehensive overview of leveraging generative AI to enhance zero-shot and few-shot learning, paving the way for more resilient and scalable solutions in environments constrained by limited data resources. These innovations promise to reshape the future of machine learning by opening new pathways for robust AI development.