What does zero-shot learning enable models to do?

Zero-shot learning is a technique in machine learning that allows models to make predictions on classes or categories that they have not been explicitly trained on. This capability is particularly valuable in scenarios where acquiring labeled training data is challenging or when a model needs to generalize to new, unseen situations.

With zero-shot learning, the model typically leverages knowledge from related tasks or domains to make predictions about these new classes. It can understand the characteristics or attributes that define these unseen classes based on its training on other, related classes, enabling it to apply that knowledge effectively.

In contrast, the other options focus on aspects of machine learning that do not align with the core principle of zero-shot learning. Therefore, recognizing the ability of zero-shot learning to predict for classes that were not included in the training data is fundamental to understanding its significance in the field of generative AI and machine learning.