Zero-Shot Learning (ZSL)

Zero-shot learning represents a paradigm shift in machine learning where models can infer unseen classes solely based on textual descriptions or attributes shared with seen classes. This ability stems from leveraging semantic embeddings that map both seen and unseen classes into a common feature space. The method employs techniques such as attribute-based classification or using pre-trained language models to generate plausible hypotheses about new categories. ZSL is particularly advantageous in applications requiring flexibility and adaptability, such as real-time AI systems reacting to novel scenarios without needing extensive retraining datasets.