What Is the Difference Between Fine-Tuning and Transfer-Learning?
Last Updated :
16 Feb, 2024
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.
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