Advancements in Zero-Shot and Few-Shot Learning: Challenges and Future Directions

Zero-shot learning (ZSL) and few-shot learning (FSL) have emerged as transformative approaches in artificial intelligence, enabling models to classify unseen categories and adapt to new tasks with minimal labeled data. ZSL leverages semantic relationships to infer properties of new classes, while FSL utilizes limited examples to rapidly generalize across tasks. These techniques address the challenges of data scarcity, which hinders traditional deep learning (DL) models that is reliant on extensive labeled datasets. Despite advancements, existing ZSL and FSL methods face limitations in generalizability, computational efficiency, and semantic representation accuracy. Many models struggle to maintain performance when applied to real-world scenarios with diverse and evolving data distributions. High computational demands and the reliance on large pre-trained models further restrict their deployment in resource-constrained environments. Additionally, issues related to overfitting and model interpretability pose significant barriers to broader adoption. This study systematically explores and integrates diverse methodologies, including transfer learning (TL), generative models, metric-based learning, and optimization techniques, to enhance the effectiveness of ZSL and FSL. By utilizing hybrid approaches, the research aims to improve model adaptability, reduce computational overhead, and ensure better alignment between seen and unseen class representations. The findings suggest that refining learning paradigms through innovative architectures and optimization strategies can lead to more robust and scalable AI models. Future advancements in meta-learning, transformers, and lightweight neural networks will further enhance the applicability of ZSL and FSL across various domains.