What is the difference between meta-learning and transfer learning?

What is the difference between meta-learning and transfer learning?

Meta-learning focuses on learning how to learn, while transfer learning applies knowledge from one task to another.

Can you give me an example of each?

Sure! In meta-learning, a model might learn to adapt quickly to new tasks, like recognizing new objects with few examples. In transfer learning, a model trained on image classification can be fine-tuned for medical image analysis.

Which one is more effective in practice?

It depends on the context. Transfer learning is often more effective when you have a lot of data for the source task, while meta-learning excels in scenarios with limited data for new tasks.

Are there any specific applications where one is preferred over the other?

Yes, transfer learning is widely used in computer vision and NLP, while meta-learning is gaining traction in robotics and personalized learning systems.