Zero shot, one shot and few shot learning: Difference between revisions

[[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, 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.

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.

One-shot learning and few-shot learning are sub-fields of machine learning and computer vision that focus on recognizing objects or classifying data with very limited data.

 

One-shot learning refers to a scenario where the machine learning model is trained on only one example of each class. The goal is to learn from this single example and generalize to new, unseen examples of the same class. In other words, the model must be able to recognize the class based on a single example and classify new, similar examples accurately.

For example, imagine you want to build a system that can recognize different handwritten digits. In a typical machine learning setup, you would need to provide the model with a large number of examples of each digit, and the model would learn to recognize each digit by finding patterns in the training data. In one shot learning, however, you might only provide the model with a single example of each digit, and the model would have to learn to recognize new instances of each digit based on that single example.