Transfer Learning, Simplified

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Transfer Learning, Simplified

AI, But Simple Issue #30

Transfer learning is a machine learning technique where a model trained on one task (with large amounts of data) is reused and adapted for a new and related task, typically with less data.

This optimization saves time and resources since it enables quicker progress and better performance when modeling the second task.

Think of how humans learn: we apply knowledge from one experience to understand and solve a new problem. Transfer learning works in the same way.

Imagine you’re really good at driving cars. When you learn how to drive a truck, you don’t start from scratch.

You already understand the rules of the road, how to use the steering wheel, and how to use the pedals. You simply need to adjust to the size of the truck and the different controls a truck has.

In this analogy, your previous driving skills are like a pretrained model, and the adjustments needed to drive the truck represent fine-tuning.

This is why transfer learning is so powerful—it allows us to build on prior knowledge rather than having to learn from scratch. This approach is effective not only in real life (where your knowledge “transfers”) but also in deep learning.

Transfer Learning in Deep Learning

Transfer learning is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing (NLP) tasks.

These tasks typically require a large amount of computational resources and time to develop effective models; transfer learning helps to reduce these costs.

The process allows for significant skill transfer between related problems, making it super useful for deep learning.

In deep learning, transfer learning takes advantage of the idea that features learned in one domain (such as recognizing edges or textures in images) can be valuable in another domain, like identifying objects or facial features.

How is it useful?

One of the key advantages of transfer learning is its ability to use data more efficiently, reducing the amount of data needed for training.

This is extremely relevant since one of the biggest challenges in deep learning is the need for large amounts of labeled data.

Training deep neural networks from scratch requires huge datasets, which are not always available, especially in more “niche” domains like medical data for rare diseases.

With transfer learning, a model can be trained on a large, diverse dataset (for instance, ImageNet) and then fine-tuned to work with a smaller and more specific dataset (like medical images).

Since the model has already learned the general features from its original training (it “knows” a lot already), it does not need as much data for the new task.

Additionally, training a deep neural network from scratch can take weeks or even months, depending on the complexity of the task.

By starting with a pre-trained model, you can bypass the initial training phase, cutting down on both time and computational costs.

Transfer learning also often results in better performance, especially when the new task shares common characteristics with the original task. For example, a model trained on recognizing objects can be adapted to specialize in detecting more specific objects (like detecting the type of plant instead of the type of object).

Summary:

• Works well with limited data; does not require large datasets

• Time and resource Savings

• Improved performance

Transfer Learning Process

There are 2 main ways to perform transfer learning:

• Developing a model on similar data

  • If you don’t have enough data for your specific task, you can first train a model using similar data and then transfer it to your task.

• Using a pre-trained model

  • Alternatively, you can start with a pre-trained model and fine-tune it for your specific task.

If you choose to develop the model, the process starts by training a base network on a related dataset and task.

Once the base model is trained, we then repurpose the learned features (or transfer them) to a new model to be trained on your specific task and dataset.

To do this:

• Train a model on a large dataset that is related to your task to capture important patterns, features, and representations that can be transferred. This will be your source task. This dataset is often used for general purpose tasks, such as image classification with ImageNet.

• The model fit on the source task can now be reused for the target task. This may involve using all or parts of the model, depending on the modeling technique used.

  • Depending on the task, you may choose to freeze the early layers of the pre-trained model (which capture low-level features such as edges or colors) and fine-tune only the later layers, which capture higher-level, task-specific features.

• The model may need to be adapted or refined (fine-tuned) on the input-output pair data available for the target task.

This approach works best when the features learned in the source task are general enough to be useful for the target task.

This form of transfer learning, known as inductive transfer, helps narrow the search for the best model by applying knowledge from a related task.

If training from scratch is too time-consuming or resource-intensive, you can opt for a pre-trained model. Many research institutions release models trained on large, challenging datasets, which can be selected as a starting point for your task.

To use a pre-trained model:

• Choose a pre-trained model that is suitable for your task from a pool of available pre-trained models.

• The model may need to be fine-tuned on the data available for the target task.

  • You might only need to adjust certain layers, or you may need to refine the entire model.

Transfer Learning Curve

Transfer learning allows the training process to be sped up greatly. Here’s what the training curve would look like:

The initial skill (before refining the model) on the source model is higher than it otherwise would be, starting higher than developing a model from scratch.

The rate of improvement of skill (slope) during training of the source model is steeper than it otherwise would be.

The converged skill of the trained model is better than it otherwise would be (higher asymptote).

Popular Models Used

Here are some commonly used pre-trained models that have revolutionized transfer learning:

VGG16 & VGG19 are widely used in image classification tasks given the fact they work well enough for their simplicity.

ResNets (Residual Networks) are famous for their deep architectures and ability to train networks with hundreds or even thousands of layers, using residual connections (or skip connections) to solve the vanishing gradient problem and improve performance.

BERT (Bidirectional Encoder Representations from Transformers) is used widely in transfer learning since it is extremely effective in the field of natural language processing (NLP). It’s pre-trained on vast amounts of text and can be fine-tuned for tasks like sentiment analysis, question answering, or text classification.

GPT (Generative Pre-trained Transformer): GPT models are another major breakthrough in NLP, excelling in tasks such as text generation, translation, and summarization.

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