Zero-shot Learning and Zero-shot Inference are related concepts in the field of machine learning, particularly in the context of models like GPT3 or image generation models like DALL-E.
However, they refer to slightly different aspects as discussed below:
Definition: Zero-shot Learning refers to the ability of a machine learning model to recognize or handle data that it has never explicitly seen during training. In Zero-shot Learning, the model is trained on a set of classes but is evaluated on a different set of classes that it has never seen before.
Application: It’s used for tasks where it’s impractical to have training data for every class the model might need to deal with, such as recognizing a wide variety of objects in images or understanding a vast range of topics in text.
How It Works: This often involves learning a rich representation of the data during training that can generalize well to new, unseen classes. The model may use metadata, attributes, or descriptions of classes to bridge the gap between seen and unseen classes.
See Also: Zero-shot Inference