Zero-Shot Learning: Enhancing Models to Recognize Unseen Classes with Minimal Data

Abstract

Zero-shot learning (ZSL) represents a paradigmatic shift in machine learning that enables models to classify instances from classes that were not observed during training. This paper presents a comprehensive analysis of contemporary zero-shot learning methodologies, with particular emphasis on generative adversarial networks (GANs) and semantic embedding approaches for enhancing recognition of unseen classes with minimal data requirements. We evaluate state-of-the-art techniques across benchmark datasets including Animals with Attributes (AWA2), Caltech-UCSD Birds (CUB-200-2011), and SUN Attribute datasets. Our experimental analysis demonstrates that generative feature synthesis combined with semantic attribute learning achieves superior performance in both conventional zero-shot and generalized zero-shot settings. The proposed framework shows significant improvements of 3-7% over existing methods in harmonic mean accuracy across multiple benchmark datasets. These findings contribute to the advancement of adaptive learning systems capable of robust performance in dynamic environments with continuously emerging object categories.