What are advanced fine-tuning techniques for AI models?

What are advanced fine-tuning techniques for AI models?

Advanced fine-tuning techniques involve methods like layer freezing, differential learning rates, and data augmentation to improve model performance.

Can you explain layer freezing in more detail?

Layer freezing is a technique where certain layers of a pre-trained model are kept unchanged during training, allowing the model to retain learned features while adapting others.

What is differential learning rate?

Differential learning rate assigns different learning rates to different layers, allowing deeper layers to learn more slowly than shallower ones, which can enhance training stability.

How does data augmentation help in fine-tuning?

Data augmentation artificially increases the size of the training dataset by creating modified versions of existing data, which helps improve model generalization.