Which transfer learning approach involves freezing the pre-trained layers and training only a few additional layers?

Transfer Learning Approach

That approach is called feature extraction with fine-tuning, also known as frozen feature extraction or shallow fine-tuning. It's a common technique in transfer learning, especially when dealing with limited data or when the pre-trained model already captures valuable features relevant to the new task.

Here's how it works:

1. Load a pre-trained model: You start with a pre-trained model on a similar task, like a pre-trained image classification model for a new object recognition task.
2. Freeze pre-trained layers: You set the trainable property of the pre-trained layers to False, essentially preventing them from updating their weights during further training. This preserves the general feature extraction capabilities learned on the original task.
3. Add new layers: You add a few new layers to the top of the pre-trained model. These new layers are specifically designed for the new task, such as a classification layer for the new object categories.
4. Train the new layers: You train only the newly added layers on your new data. This allows the model to adapt the pre-extracted features to the specific needs of the new task.

Benefits of this approach:

• Faster training: By freezing the pre-trained layers, you significantly reduce the number of parameters to train, resulting in faster training times.
• Leverage pre-trained knowledge: You leverage the valuable features learned by the pre-trained model, reducing the need for large amounts of training data for the new task.
• Improved performance: This approach can often achieve good performance on the new task even with limited data, as it builds upon the strong foundation of the pre-trained model.

Limitations:

• Potentially limited adaptation: Freezing the pre-trained layers can restrict the model's ability to fully adapt to the new task, especially if the tasks are significantly different.
• Choosing the right layers: Selecting the optimal layers to freeze and unfreeze can require experimentation and may not be straightforward.

Overall, feature extraction with fine-tuning is a powerful and efficient approach for transfer learning, particularly when the new task is related to the original task and data is limited. It's a versatile technique that has proven successful in various domains like computer vision, natural language processing, and many more.

I hope this explanation clarifies the concept and its applications! Let me know if you have any further questions.