Comparative Analysis of Core Methods in the Few-Shot Learning Area

Few-shot learning (FSL) aims to address the critical challenge of models struggling to learn from very limited examples. It becomes extremely important when data collection is difficult or costly. This paper provides a comprehensive comparative analysis of core FSL methods, focusing on meta-learning approaches and data augmentation strategies. The paper examines metric-based meta-learning algorithms-including Matching Networks, Prototypical Networks, and Relation Networks-alongside augmentation-based methods such as traditional data augmentation and generative models. The comparative study reveals that metric-based approaches leverage learned embedding spaces and distance metrics to achieve strong classification performance with minimal data. Meanwhile, augmentation-based techniques significantly improve model generalization by synthetically expanding the support data, with generative augmentation yielding notable accuracy gains. Our findings highlight the strengths and limitations of each method under various conditions. In summary, this work underscores the value of combining meta-learning with data augmentation to tackle data scarcity, and it outlines key challenges, such as domain shift, scalability, or theoretical gaps, to guide future few-shot learning research and applications.