Zero-shot learning

Zero-shot learning is a machine learning technique in which a model learns skills to solve tasks for which it has not seen specific examples during training. Instead of relying on existing data, the model uses information from other related tasks to tackle new tasks. By using feature descriptions or semantic relationships, zero-shot learning can transfer a model's knowledge and enable it to make predictions or classifications in unknown domains. This technique makes it possible to increase the flexibility and adaptability of AI models and reduce the need for extensive training data. Other methods are fine-tuning and few-shot learning.