Few-shot learning

Few-shot learning refers to a type of machine learning in which an AI model learns to master new tasks with only a limited number of training examples. In contrast to conventional machine learning, which requires a large amount of training data, Few-shot Learning enables rapid learning and adaptation to new tasks, even when only a few examples are available. This is achieved by the model utilising existing knowledge and experience from previous tasks and transferring it to new situations. Few-shot learning is particularly useful in situations where it is difficult to collect extensive training data or when rapid adaptability is required. It enables efficient and flexible use of AI models in different application areas. Other methods include fine-tuning and zero-shot learning. Other methods are fine-tuning and zero-shot learning.

Black box phenomenon