What is Few-Shot Learning?

Few-Shot Learning is a machine learning approach where AI models can learn to perform new tasks or recognize new patterns using only a small number of training examples, typically between 2-10 examples per category or task.

Few-shot learning enables AI systems to quickly adapt to new domains or tasks without requiring large datasets, making AI more flexible and practical for real-world applications where data may be scarce.

Few-shot learning works by leveraging pre-existing knowledge and patterns learned from previous tasks, using techniques like meta-learning, transfer learning, and contextual learning to generalize from minimal examples.

A language model shown 3 examples of translating English to a rare language can then translate new English sentences to that language, despite having minimal training data for that specific language pair.

Connected to Transfer Learning, Meta-Learning, Zero-Shot Learning, One-Shot Learning, and Prompt Engineering techniques.