by: https://x.com/deeplearnerd

In our previous blog, we laid the foundation for deep learning in computer vision by covering CNNs, modern architectures like ResNet and VGG, and the basics of training deep models. However, after receiving valuable feedback, I realised that one important topic Transfer Learning—wasn't explored in enough detail. In this post, we’ll dive deeper into transfer learning, examining its mechanisms and why it’s such a powerful tool in accelerating model training and improving accuracy, particularly in tasks with limited labeled data.

This blog is structured around three major sections:

• Transfer Learning: A Deeper Revisit - Exploring the core concepts of transfer learning, its benefits, and when to use it.
• More Architectures: UNet and AlexNet - Understanding the architectures of UNet for segmentation and AlexNet for image classification.
• Evaluation Metrics - Key metrics like IoU, Dice Coefficient, and accuracy for assessing model performance.

Transfer Learning: A Deeper Revisit

Analogy First

Think of Transfer Learning like Naruto mastering the Rasenshuriken after already knowing the Rasengan.

At first, Naruto learns the basic Rasengan from Jiraiya. It takes time, effort, and many failures to get it right. This is similar to training a deep learning model from scratch—it requires a huge dataset and a lot of computation.

Later, when Naruto wants to develop the Rasenshuriken, he doesn’t start from zero. He transfers his knowledge of Rasengan and adds wind chakra to it. This speeds up his learning process significantly compared to if he had tried to create the Rasenshuriken from scratch.

This is exactly how Transfer Learning works in deep learning. A model (like ResNet) is first trained on a large dataset (like ImageNet). Then, instead of training a new model from scratch, we take this pretrained model and fine-tune it on a smaller, task-specific dataset—just like Naruto fine-tuned Rasengan to create Rasenshuriken!

A more Practical Example

Transfer Learning allows a model trained for one task to be adapted to another, reducing the need for training from scratch.

For example, a ResNet model pretrained on ImageNet can be fine-tuned to detect pneumonia in X-rays or tumors in MRI scans by modifying only the final layers.

Training a medical model from scratch is costly and time-consuming, but Transfer Learning enables high accuracy with less data and computing power, making disease detection faster and more efficient.