Few-Shot Learning (FSL) is crucial for tasks where collecting a large amount of labeled data is impractical or too costly. It challenges the conventional machine learning paradigm by training models on limited examples while still maintaining high performance. This is achieved through techniques such as meta-learning, where the model learns to learn by generalizing from previous tasks, and transfer learning, which involves transferring knowledge from related tasks with ample data to the task with limited data. FSL is particularly significant in fields like medical diagnosis, where annotated examples are scarce but require high accuracy.
Historical overview: The concept of Few-Shot Learning started to gain prominence in the AI research community around the early 2010s, alongside the rise of deep learning technologies. It emerged as a solution to the data scarcity problem in specific domains.
Key contributors: The development of Few-Shot Learning has been a collaborative effort across various research institutions and technology companies. Prominent figures include Fei-Fei Li, who contributed significantly to the early work on image recognition with few examples, and researchers in the field of meta-learning, such as Chelsea Finn for her work on model-agnostic meta-learning (MAML).