How can we enhance transfer learning techniques in AI?

How can we enhance transfer learning techniques in AI?

To enhance transfer learning, we can focus on better pre-training models, fine-tuning strategies, and domain adaptation.

What are some effective pre-training models for transfer learning?

Popular pre-training models include BERT for NLP tasks and ResNet for image classification. They provide strong feature representations.

How does fine-tuning differ from pre-training?

Fine-tuning adjusts a pre-trained model on a specific task, while pre-training involves training a model on a large dataset to learn general features.

Can you give examples of domain adaptation techniques?

Sure! Techniques include adversarial training, feature alignment, and using domain-invariant representations.

What challenges do we face in transfer learning?

Challenges include negative transfer, domain shift, and the need for large labeled datasets in the target domain.