What is few-shot learning

Few-shot learning is a machine learning approach that enables models to recognize new classes or concepts with only a limited number of training examples. It represents a significant shift from traditional models that require large datasets for each category.

This approach utilizes techniques such as meta-learning, transfer learning, or metric learning. Meta-learning trains models to rapidly adapt to new tasks by simulating few-shot scenarios during training. Transfer learning leverages knowledge gained from large datasets for related tasks. The method is particularly valuable for domains where data collection is costly or time-prohibitive, such as specialized medical imaging or rare defect identification in manufacturing.

Its primary value lies in overcoming the high data dependency barrier of conventional AI, enabling quicker deployment in new, data-scarce scenarios. It improves model adaptability and reduces the expense and time needed for data gathering and annotation. Applications span personalized recommendations, rapid diagnostics, and interpreting rare events.