One-shot learning tests whether a model can use a single example to infer the important structure of an unfamiliar task. The example may be provided during training, adaptation, or directly in the inference context.
Success requires more than copying the demonstration. A useful one-shot learner identifies the task intent and transfers it to changed objects, states, or conditions while avoiding irrelevant details from the example.
