One-shot learning addresses the challenge of training models with very limited data, often just one example per class. This technique is particularly important in fields like computer vision and natural language processing where acquiring extensive labeled datasets can be difficult or impractical. One-shot learning models often rely on methods such as Siamese networks, which use pairs of inputs to learn similarities, or Bayesian networks, which incorporate prior knowledge to make predictions based on minimal data. These models are designed to generalize from the limited examples by leveraging prior knowledge or leveraging tasks that allow them to understand the underlying structure of the data.
Historical Overview: The concept of one-shot learning was first introduced in the 2000s, with significant advancements in the mid-2010s due to improvements in deep learning. It gained substantial attention in 2015 with the publication of seminal papers on Siamese networks and related architectures that effectively implemented one-shot learning in practical applications.
Key Contributors: Fei-Fei Li and her colleagues at Stanford University made significant contributions to the development of one-shot learning through their work on the ImageNet dataset and related research in computer vision. Researchers from Google DeepMind, such as Oriol Vinyals and his team, have also been pivotal with their work on matching networks and other architectures that facilitate one-shot learning.