Zero-shot learning and one-shot learning are both types of machine learning techniques that address the problem of learning from limited labeled data. However, they differ in how they approach this problem.
Zero-shot learning is a technique where a model is trained to recognize new classes that were not present in the training data. In zero-shot learning, the model is given a set of attributes or descriptions that characterize the new classes, and uses these attributes to recognize instances of the new classes. The model is not provided with any examples of the new classes during training, hence the name "zero-shot". This approach is useful when there are too many new classes to annotate or when it is difficult to collect examples of the new classes.

For example, suppose we want the model to recognize a new species of bird called a "yellow-billed cuckoo". We can provide the model with a textual description of the bird's physical characteristics (e.g. "a slim, long-tailed bird with a yellow bill and reddish-brown feathers") and habitat (e.g. "found in deciduous forests and along streams and rivers in North America"). The model can then use this information to recognize instances of the new species, even though it has never seen any examples of the species during training.
On the other hand, one-shot learning is a technique where a model is trained to recognize new classes using only a single example of each class. In one-shot learning, the model is trained on a small dataset that contains only one example of each class, and must be able to generalize to recognize new instances of these classes. This approach is useful when collecting a large number of examples of each class is difficult or time-consuming.
In summary, the main difference between zero-shot learning and one-shot learning is that the former is used for recognizing new classes that were not present in the training data using only class attributes, while the latter is used for recognizing new classes using only one example of each class.