Few-shot learning and zero-shot learning are two approaches aimed at improving the performance of machine learning models when faced with limited data. The primary difference between them lies in the amount of experience the model has with a particular task or class before making predictions. In few-shot learning, the model is trained on a small number of examples (or "shots") for each category it needs to recognize. For instance, if a model is tasked with recognizing different species of birds, it may be given only a few images (say, 5) of each bird species to learn from. This method is particularly useful when collecting large datasets for every category is impractical or costly.
Conversely, zero-shot learning takes a different approach by allowing the model to make predictions on classes it has never encountered during training. Instead of providing examples from the target classes, the model relies on knowledge transfer from related classes or the use of auxiliary information. For example, if a model has been trained on identifying different animals but has never seen a "zebra," it might still be able to classify it correctly based on its understanding of "striped animals" or "horses." In this case, the model utilizes semantic information about the concept of a zebra without any direct training examples.
Both learning paradigms address the challenge of limited labeled data but do so in distinct ways. Few-shot learning improves model accuracy through a minimal dataset, while zero-shot learning extends the model’s capabilities to new, unexplored categories. Developers can choose between these techniques based on the availability of training data and the specific requirements of their applications. For example, few-shot learning could be more effective in situations with similar but distinct categories, while zero-shot learning could be advantageous in dynamic environments where new categories frequently emerge.