One-shot learning is a specialized form of machine learning (ML) that trains a model to carry out a task by leveraging a minimal number of examples. It represents a crucial research domain in ML, as it holds the promise of substantially cutting down the data volume and computational resources needed for model training.
One-shot learning proves especially valuable in situations where acquiring a large quantity of labeled examples is challenging or unfeasible, like when dealing with rare or hard - to - access data. It also comes in handy when the data is highly imbalanced, as it can mitigate the risk of the model being skewed towards the majority class.
This learning approach has numerous practical applications across various fields, including image recognition, speech recognition, and natural language processing. For instance, in robotics and autonomous vehicles, where labeled data is frequently in short supply or difficult to procure, one-shot learning can be employed to train models to identify new objects.