Transfer Learning: The Ultimate Guide for Beginners

Topics Covered

1. What is Transfer Learning and How it works?

2. Real life example of transfer learning.

3. Benefits of using Transfer learning

4. Ways of doing transfer learning.

5. Conclusion

What is Transfer Learning?

Transfer learning is a powerful machine learning technique where knowledge gained from one task is applied to improve the performance of another, related task. In simpler terms, you can take a pre-trained model and adapt it to solve a new problem instead of building everything from scratch.

Imagine you have trained a language model that can translate English to Spanish. Now, you need to build a model for translating English to Hindi. Transfer learning allows you to leverage the existing knowledge of understanding English from the Spanish model and simply adjust the model to focus on converting Hindi text.

Transfer learning is mainly used in computer vision and natural language processing-related problems.

In computer vision, neural networks are designed to learn fundamental features like edges in the initial layers, more complex patterns and forms in the middle layers, and task-specific representations in subsequent layers. To take advantage of this, A common practice in transfer learning leverages this hierarchical learning by utilizing the convolutional layers (specifically the first and middle layers) of a pre-trained model to tackle new problems.

Let’s Understand it by Real life example,

Imagine two students, A and B. Student A has spent years learning the fundamentals of art, including identifying basic visual features like lines, curves, and textures. This knowledge allows them to successfully identify the presence of humans in images.

Now, a new student, B, joins the class and wants to learn to identify animals in images. It would be much more efficient for B to leverage A’s knowledge of basic visual features rather than starting from scratch. B can use A’s understanding of lines, curves, and textures as a foundation, and then focus on acquiring additional skills specific to animal identification, such as recognizing distinctive features like paws, tails, and fur patterns.

This is essentially how transfer learning works. We utilize pre-existing knowledge from one task (human detection in this case) and apply it to a related task (animal detection), resulting in a faster and more efficient learning process.

Benefits of using Transfer learning

Here are some key benefits of using transfer learning

• Improved performance with less data: You can achieve better results compared to training from scratch, especially with limited data.
• Reduced training time and complexity: You can significantly cut down training time and simplify the model by building upon pre-trained knowledge.
• similar tasks: You can quickly adapt existing knowledge to new tasks that share similar characteristics.

Ways of doing transfer learning

There are two popular ways of doing transfer learning.

Feature Extraction:

This is the most common type of transfer learning, where the pre-trained model is used to extract features from the input data. These features are then used to train a new model for the specific task. This is often used in computer vision tasks, where a pre-trained model like VGG16 or ResNet can be used to extract features like edges, textures, and shapes from images. These features can then be used to train a new model for tasks like object detection, image classification, or image segmentation.

Fine Tuning:

Fine-tuning is another prevalent approach where a pre-trained model is further trained on the target task. During fine-tuning, the weights of the initial layers (capturing general features) are typically frozen, and only the weights of later layers are adjusted to adapt to the specific task. This allows for a more task-specific adaptation while retaining the knowledge gained from the pre-training.

Conclusion

To Summarize it, transfer learning is like borrowing knowledge from one task to ace another. It’s a way to make models smarter without starting from scratch. Whether you’re dealing with pictures or words, transfer learning helps when you’re low on data, want to speed things up, or need to tackle a similar task.

I hope you’ve gained a clear understanding of the Transfer Learning. Feel free to share your thoughts or connect with me on LinkedIn to continue the conversation.