What is a Pre-Trained Model?

Instead of building a model from scratch, we can use a pre-trained model trained on a similar problem. This approach is more efficient and effective, especially when the tasks are closely related.

However, if your current task is very different from the one the model was originally trained on, predictions may be inaccurate. That’s why it’s important to choose a pre-trained model wisely.

The Keras library provides several pre-trained models, many of which are trained on the ImageNet dataset (with over 1.2 million images). These models can recognize up to 1,000 common object categories like animals, household items, and vehicles.

When using Transfer Learning, we often generalize the model to work with data outside the original dataset using techniques like fine-tuning.

Fine-Tuning Pre-Trained Models

There are three major strategies for fine-tuning:

Feature Extraction: Use the pre-trained model as a fixed feature extractor by removing the output layer and using the rest of the network on your new data.

Reuse Model Architecture: Keep the architecture of the pre-trained model and initialize training using it.

Freeze Some Layers: Freeze initial layers (to preserve learned features) and retrain higher layers to adapt the model to the new task. You can experiment to decide how many layers to freeze or retrain.

Scenarios for Using Pre-Trained Models

Small dataset, high similarity: No need to retrain the model fully. Just customize the output layers (e.g., change from 1000 categories to 2 for classifying cats and dogs).

Small dataset, low similarity: Freeze the initial layers (k) and retrain the remaining layers (n-k). This allows the model to learn specific features of your data.

Large dataset, low similarity: Train a model from scratch. With enough data, this approach is more effective.

Large dataset, high similarity: This is the best-case scenario. Use the pre-trained model’s weights and architecture, and retrain with your own data.

Inductive Learning and Inductive Transfer

In deep learning, inductive transfer refers to narrowing down the model search space in a useful way. The model trained on one task is adapted to a related task, improving performance with less effort.

Two Approaches to Transfer Learning

a. Develop Model Approach

Select Source Task: Choose a task with plenty of data.

Build Source Model: Train a high-performing model for the task.

Reuse Model: Use parts of the model as the base for a new task.

Tune Model: Adjust the model to fit the new task using available data.

b. Pre-Trained Model Approach

Select Source Model: Choose from available pre-trained models.

Reuse Model: Use the model as the starting point.

Tune Model: Modify it for your specific task.

When Should You Use Transfer Learning?

Transfer Learning helps when:

You want to save training time.

You need better initial performance.

You aim for higher final model accuracy.

Benefits include:

Higher Start: You begin training with a more skilled model.

Higher Slope: Faster improvement during training.

Higher Asymptote: Better final performance.

Conclusion

To sum up, Transfer Learning is a powerful tool in Deep Learning. We explored how and when to use pre-trained models and fine-tuning methods. It helps save time, resources, and often improves accuracy.

Still, it’s important to evaluate whether a pre-trained model fits your specific problem. As Transfer Learning continues to evolve, new techniques are emerging to make it even more effective.

If you have any questions, feel free to reach out in the comments.