Transfer Learning

Created: 10 Aug 2025.

As I delved into Generative AI with Deep Learning, an area that I wanted to understand more on was the transfer of existing models to new AI systems to allow the new system to build on top of that.

To me, this was quite an important concept. In the animal kingdom, I would say that the big different between humans and animals is our ancestor’s ability to record down knowledge (model in the AI equivalent sense) in the form of books, web sites etc. and for the next generation to learn from that and build on top of that knowledge. Transfer Learning was similar to this. It allows us to build on top of an existing model, which is quite mind boggling!

Transfer learning is a machine learning technique that reuses a pre-trained model as a starting point for a new, related task. Instead of training a model from scratch, which is computationally expensive and requires vast amounts of data, you can leverage a model that has already learned a broad set of features from a massive, general dataset. The “knowledge” is transferred by using the pre-trained model’s learned parameters (weights and biases) and adapting them to the new task, a process known as fine-tuning.

Fine-Tuning

Fine-tuning is the most common mechanism for transfer learning, especially with modern deep learning models. It involves taking a pre-trained model and continuing to train it on a new, task-specific dataset. The goal is to slightly adjust the model’s parameters to adapt to the new data while preserving the general knowledge it learned during its initial, large-scale training.

Here is a breakdown of how this is done.

Model Selection

First, you select a pre-trained model that’s suitable for your problem. For example, if you’re building a new image classification model for medical scans, you might start with a model like ResNet or VGG that was pre-trained on the ImageNet dataset, which contains millions of general images.

Freezing Layers

You “freeze” the early layers of the network, which have learned general features like edges, textures, or basic shapes. This prevents these layers from being updated during the new training process, preserving their valuable, low-level knowledge.

Retraining Later Layers

The later layers of the network are typically more specialized. You “unfreeze” these layers and train them on your new, specific dataset. You might even replace the final output layer of the model with a new one that matches the number of classes or outputs required for your new task.

Learning Rate Adjustment

A crucial detail is using a much lower learning rate for fine-tuning than for the original training. This ensures that the model makes small, careful adjustments to its weights rather than overwriting its pre-trained knowledge with large, disruptive changes.

Feature Extraction

Feature extraction is a simpler approach, where the pre-trained model is used as a fixed feature extractor. This method is often used when the new dataset is small and you want to avoid the risk of the model over-fitting to the limited data.

Here is how it works:

Black Box Learning

You take the pre-trained model and feed your new data through it. However, you stop the process at a specific layer before the final output. The output of this intermediate layer is a set of learned features, which are essentially a rich numerical representation of your input data.

Training a New Classifier

You take these extracted features and use them as input for a new, simpler machine learning model, such as a logistic regression classifier or a small neural network. This new model is trained from scratch on your task-specific data.

Preserving Knowledge

This method effectively uses the pre-trained model’s knowledge as a powerful tool to translate raw data into a more meaningful representation, without changing any of the original model’s parameters. This is like using a skilled art critic (the pre-trained model) to describe a painting (your data) with detailed observations (the features), and then using those observations to teach a beginner (your new classifier) how to make a final judgement.