What Is the Difference Between Fine-Tuning and Transfer-Learning?

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.