GZSL Method

Generalized zero-shot learning (GZSL) aims to train classifiers capable of recognizing objects or events from classes unseen during training, leveraging auxiliary information like attributes. Current research focuses on improving feature extraction, often employing generative models and self-supervised learning techniques to enhance the representation of visual or acoustic data, and on developing methods that handle the dynamic addition of new classes over time (continual GZSL). These advancements are significant because they enable more robust and adaptable AI systems, with potential applications in areas like sound event classification and image recognition where exhaustive training data is unavailable or impractical to obtain.