A machine learning technique termed as zero-shot learning (ZSL) trains AI models to recognize and categorize things or ideas without having previously encountered them. In general, a branch of machine learning termed Zero-Shot Learning (ZSL) enables models to identify and classify instances of classes they were not exposed to during training. According to experts, Zero-shot learning uses auxiliary information, such as semantic attributes, text descriptions, or relationships between classes, to bridge the gap between seen and unseen categories, in contrast to traditional supervised learning, which necessitates substantial labeled data for each category.There are three main types of zero-shot learning such as Conventional Zero-Shot Learning (CZSL), Generalized Zero-Shot Learning (GZSL), and Transductive Zero-Shot Learning.Zero-Shot Learning enables models to handle real-world scenarios with unseen categories, minimizes time and resources spent on data collection and labeling, and supports growing systems without requiring frequent retraining.
