Zero shot, one shot and few shot learning

Zero shot learning is when you have no examples in the prompt. One shot learning is when you have 1 example in the prompt. Few shot learning is when you have a few examples in the prompt. all of these techniques allow the machine learning model to learn with limited or no labeled data.

Zero Shot Learning

Zero-shot learning, one-shot learning, and few-shot learning are all machine learning techniques used to train models with limited or no labeled data.

Zero-shot learning: In this technique, a model is trained to recognize new objects or concepts that it has never seen before. The model is trained on a set of known objects or concepts, and during inference, it is presented with new objects or concepts. The model uses its knowledge of the known objects or concepts to classify the new objects or concepts.

Example

For example, a model trained on images of dogs and cats may be presented with an image of a zebra during inference. Even though the model has never seen a zebra before, it m ay be able to classify it correctly based on its knowledge of other animals.

Few Shot Learning

Example

For example, a model trained on images of different types of fruits can be tested on a new image of a fruit it has never seen before. Using few-shot learning, the model can recognize the new fruit from just a few images.