Few-shot learning is a type of machine learning task where only a few examples of a given concept are provided. It allows systems to learn from a small number of examples and apply that knowledge to a larger number of tasks in a controlled environment.
Few-shot learning is an appealing method in computer science as it eliminates the need from extensive data collection and annotation processes while still allowing for the development of effective machine learning models. Compared to traditional supervised machine learning, few-shot learning offers the advantages of faster training times, reduced storage requirements and improved model generalization performance.
The most common type of few-shot learning is called near one-shot learning, meaning a model is trained on just a few examples of the concept it needs to identify. The most popular type of near one-shot learning is known as the Siamese network. This type of network involves using an algorithm to identify whether two given inputs are similar or dissimilar in terms of understanding the task it needs to complete.
The goal of few-shot learning is to accurately and quickly classify images or text by providing only a few examples of a concept. A common use case is for medical diagnostics, particularly in identifying unseen diseases using a small amount of data.
Few-shot learning is seen as a promising alternative to traditional supervised machine learning, due to its potential for rapidly generating accurate results with fewer data points. While there are some challenges involved such as generalizing the learned model in the presence of data outliers, research is ongoing to improve its accuracy and utility.
