What does "few-shot learning" refer to in generative AI?

Few-shot learning in generative AI refers to the ability of a model to perform tasks with minimal training examples. This approach is particularly valuable in scenarios where obtaining large datasets is impractical or costly. In few-shot learning, the focus is on enabling the model to generalize from just a handful of examples and still perform well in the given task.

This is typically achieved through various techniques, such as meta-learning, where the model learns how to learn from limited data, or by leveraging prior knowledge from similar tasks to aid in making predictions. The efficiency of few-shot learning allows for rapid adaptation to new tasks without the need for extensive retraining, making it a flexible solution in evolving domains.

The other options refer to concepts that do not align with the core definition of few-shot learning. Learning with extensive examples would imply needing a large dataset, which is the opposite of what few-shot learning aims to achieve. Training deep networks pertains to the type of architecture or learning strategy but does not specifically address the efficiency of learning from a few examples. Reducing model complexity focuses on streamlining models for better performance or efficiency, rather than the capacity to learn effectively from limited data.