Few-shot learning addresses the challenge of data scarcity in training machine learning models. Traditional models require vast amounts of labeled data to perform well, but few-shot learning techniques enable models to generalize from only a handful of examples. This is achieved by leveraging prior knowledge, either from related tasks or pretrained on large datasets, which is then adapted to new tasks with minimal additional input. Few-shot learning is especially valuable in domains where collecting extensive data is impractical or impossible, such as rare disease diagnosis, low-resource languages, or personalized user interactions.
Historical overview: Few-shot learning emerged as a notable concept within the machine learning community around the early 2010s. It gained popularity as part of the broader exploration into more efficient and adaptable AI systems, which sought to mimic human-like learning efficiency from limited examples.
Key contributors: Significant early contributions to the field of few-shot learning were made by researchers in the domain of meta-learning and transfer learning, which form the theoretical backbone for many few-shot learning algorithms. The development of models like Siamese Networks and Prototypical Networks in the mid-2010s were pivotal, helping establish practical approaches to few-shot learning.