Prototypical Neural Network

Prototypical neural networks are a class of machine learning models that learn by representing data categories as prototypes, typically cluster centroids in a feature space. Current research focuses on improving their performance in few-shot learning scenarios, particularly addressing challenges like cross-domain transferability and out-of-distribution detection, often employing techniques like normalizing flows and adaptive weighting to enhance robustness and interpretability. These advancements are impacting various fields, including image classification, natural language processing, and medical diagnosis, by enabling efficient learning from limited data and providing more explainable AI models.