Fine-tuning and feature-based approaches are two common techniques in transfer learning, a machine learning method where a pre-trained model is reused as a starting point for a new task.
Fine-tuning involves adjusting the weights of a pre-trained model on a new dataset. This method is more computationally intensive but can lead to better performance, especially when the new task is similar to the original task.
Feature-based approaches, on the other hand, extract features from a pre-trained model and use them as input for a new model. This method is less computationally intensive and can be effective when the new task is different from the original task.
Key Differences:
| Feature | Fine-tuning | Feature-based |
|---|---|---|
| Model Modification | Adjusts weights of pre-trained model | Extracts features, trains new model |
| Computational Cost | Higher | Lower |
| Task Similarity | Best for similar tasks | Can be used for diverse tasks |
| Data Requirements | More data needed | Less data needed |