Zero-shot learning is a type of machine learning in which a model is trained on a set of tasks and then tested on a different set of tasks without any additional training. This is in contrast to traditional machine learning, in which a model is trained on a specific task and then tested on the same task. Zero-shot learning is a challenging problem, but it has the potential to enable machines to learn more efficiently and to perform tasks that are difficult or impossible for humans to do.
There are two main types of zero-shot learning: unseen class zero-shot learning and unseen task zero-shot learning. In unseen class zero-shot learning, the model is trained on a set of classes and then tested on a different set of classes. In unseen task zero-shot learning, the model is trained on a set of tasks and then tested on a different set of tasks.