generalized zero-shot learning

Generalized zero-shot learning is a method in machine learning where a model learns to recognize new categories it has never seen before by using descriptive information, like their attributes or characteristics. Unlike traditional models that require examples of each category, this approach can identify both familiar and unfamiliar classes at the same time by leveraging knowledge from related categories. It’s useful in situations where collecting training data for every possible category is impractical, allowing systems to make informed guesses about new, unseen items based on their descriptions and similarities to known categories.