one-shot-learning

One-shot learning is particularly useful in scenarios where obtaining a large number of training samples is impractical. It leverages techniques such as metric learning, where models learn to identify similarities between examples, allowing them to generalize from just one instance. This concept is often applied in computer vision tasks, such as facial recognition, where acquiring many labeled images for each individual is challenging.