Answer: Fine-tuning adapts a pre-trained model on a specific task with modest changes, while transfer learning employs knowledge gained from a pre-trained model to enhance performance on a different but related task.
Fine Tuning vs Transfer Learning: Comparison
Aspect | Fine-Tuning | Transfer Learning |
---|---|---|
Objective | Adapt pre-trained model to a specific new task | Leverage knowledge from a pre-trained model to enhance performance on a related task |
Training Approach | Train the entire model with new data | Often freeze some layers of pre-trained model and train specific layers on the new task |
Data Requirement | Typically requires more data specific to the new task | Can be effective with smaller datasets due to leveraging pre-trained knowledge |
Use Case | When task-specific data is available and computational resources allow full retraining | When limited labeled data or computational resources are available, and tasks share similarities |
Complexity | More complex as it involves retraining the entire model | Less complex as it often involves freezing some layers and training only specific layers |
Example | Fine-tuning BERT for sentiment analysis on a new dataset | Using a pre-trained ImageNet model to improve image classification on a new dataset |
In summary, while fine-tuning involves adapting a pre-trained model to a new task with additional training, transfer learning utilizes knowledge from a pre-trained model to enhance performance on a related task, often with fewer training data or computational resources.