Transfer Learning in Deep Learning: Leveraging Pre-trained Models for Faster and Better Training

5 min readNov 2, 2023

“Transfer Learning will be the next driver of Machine Learning success. — Andrew Ng”

Transfer learning in simple words is knowledge transfer.

Deep learning has revolutionized the field of artificial intelligence, enabling machines to learn and make predictions or decisions from complex data. One of the key techniques that has contributed to the success of deep learning is transfer learning. Transfer learning allows us to leverage knowledge gained from solving one problem and apply it to a different, but related problem. This approach has proven to be invaluable in many domains, including computer vision, natural language processing, and more.

Introduction

In traditional machine learning, models are trained from scratch on a specific dataset for a specific task. The model learns the underlying patterns and features directly from the data. However, in deep learning, especially for complex tasks and large datasets, training models from scratch can be computationally expensive and time-consuming.

This is where transfer learning comes in. Instead of starting from scratch, we begin with a pre-trained model that has been trained on a large dataset for a related task. The idea is that the knowledge gained by the model in its previous task can be transferred and applied to the new task, often with minimal fine-tuning.

1. Understanding Transfer Learning

a. Pre-training on a Source Task:

In the first step, a neural network model, often a deep convolutional neural network (CNN) for computer vision tasks, is trained on a large dataset for a source task. This source task is typically a generic problem like image classification on a large dataset like ImageNet.
During training, the network learns to extract features from the input data (e.g., images) and uses these features to make predictions for the source task.

b. Feature Extraction:

After the initial training is complete, the early layers of the network have learned to extract useful and general features from the data, which can be applied to a wide range of related tasks.
These early layers form a feature extractor, which can be thought of as a powerful tool for understanding the underlying structure of the data.

c. Fine-tuning for Target Task:

For the target task (which could be a more specific problem), a new dataset is collected. This dataset might be smaller than the original dataset used for pre-training and may be related but not identical in nature.
Instead of training a neural network from scratch, which would require a large amount of data, the pre-trained model is used as a starting point.
The layers of the pre-trained network are adjusted (fine-tuned) to make them more specific to the new task. This can involve unfreezing some or all of the layers and allowing them to be updated during training.

d. Training on Target Task:

The model is then trained on the new dataset for the target task. The fine-tuning process updates the weights in the network to adapt it to the specifics of the new data.
Since the early layers have already learned useful features, the model can learn task-specific features more efficiently and with less data compared to training from scratch.

e. Evaluation and Deployment:

Once the fine-tuning is complete, the model is evaluated on a separate test set for the target task to assess its performance.
If the model meets the desired performance criteria, it can be deployed for making predictions on new, unseen data.

2. Benefits of Transfer Learning

a. Faster Training

Since the model has already learned general features from the pre-training phase, it requires fewer iterations to adapt to the new task. This significantly reduces the time and computational resources needed for training.

b. Less Data Dependency

Transfer learning can be particularly advantageous when you have a limited amount of data for your specific task. The pre-trained model has already learned useful features from a large dataset, which can be applied to your smaller dataset.

c. Improved Generalization

Pre-trained models have learned rich feature representations from diverse data. This means they often have a better understanding of the underlying structures in the data, leading to improved generalization performance on a new task.

3. Types of Transfer Learning

a. Feature Extraction:

In this approach, the pre-trained model’s convolutional base (or earlier layers) is used as a fixed feature extractor. The weights of these layers are frozen, and a new classifier (often a few dense layers) is added on top.
The input data is passed through the fixed convolutional layers, and the features learned by these layers are then used as input to train the new classifier for the target task.
This method is particularly useful when the dataset for the target task is small, as it leverages the general features learned by the pre-trained model.

b. Fine-tuning or Fine-tuning All Layers:

Unlike feature extraction, in fine-tuning, not only are the new layers added on top, but some or all of the pre-trained layers are unfrozen.
During training, both the new layers and the unfrozen layers from the pre-trained model are updated with backpropagation using the target task’s data.
This approach can be beneficial when the target task dataset is larger and more similar to the source task dataset, allowing the model to adapt more effectively to the new task.

c. Fine-tuning Some Layers:

In this variation of fine-tuning, only a subset of the pre-trained layers is unfrozen, while the rest remain frozen.
This allows for a more controlled adaptation to the new task, as it limits the number of layers that are updated, preventing overfitting, especially when the source and target tasks are only loosely related.

d. Pre-training and Multi-Task Learning:

In this approach, a model is pre-trained on a large dataset for a source task. After pre-training, additional task-specific heads (outputs) are added to the model, and the model is then fine-tuned on multiple related tasks simultaneously.
This allows the model to learn representations that are useful for multiple tasks, potentially improving performance across all tasks.

e. Zero-Shot Transfer Learning:

In zero-shot transfer learning, a model is trained on a source task but is evaluated on a completely different target task. The model generalizes its learned features to perform tasks it has never seen before.
This approach is used to test the ability of a model to learn generic features that are broadly applicable.

f. One-Shot Transfer Learning:

Similar to zero-shot, in one-shot transfer learning, the model is trained on a source task. However, instead of being evaluated on a completely different task, it is evaluated on a target task with very limited data.
This is a challenging scenario where the model has to quickly adapt to a new task with very little training data.

These different types of transfer learning allow practitioners to adapt pre-trained models to a wide range of tasks, from those with similar datasets to tasks that are completely different but can still benefit from the learned representations. The choice of transfer learning approach depends on factors like dataset size, similarity between tasks, and available computational resources.