Key Differences between Transfer Learning and Fine-Tuning
Aspect
Transfer Learning
[[Fine-tuning|Fine-Tuning]]
Block diagram
Scope
Broad concept of reusing a pre-trained model for a new task
Specific technique of adapting a pre-trained model
Process
Often involves using the pre-trained model as-is or modifying the last layer
Involves additional training with a new dataset
Layer Adjustments
Generally keeps all layers, sometimes adds a new output layer
Freezes early layers, trains later ones (or the entire model)
Goal
Leverage general features learned by the pre-trained model
Make the model specific to the new task’s requirements
Purpose
Reusing a pre-trained model to avoid training from scratch, leveraging learned patterns (such as visual features, language structures, etc.) from the source task.
Adapt the pre-trained model to perform better on the target task by allowing some degree of learning specific to the new dataset.
Example
Using a model trained on ImageNet to perform object detection or image classification on a different dataset.
Using BERT pre-trained on general text data and fine-tuning it on a specific task, like sentiment analysis or named entity recognition.
