What is transfer learning in image recognition?

What is transfer learning in image recognition?

Transfer learning is a technique where a model developed for a specific task is reused as the starting point for a model on a second task.

How does it improve the performance of image recognition models?

It allows models to leverage knowledge from previously learned tasks, reducing training time and improving accuracy, especially with limited data.

Can you give an example of transfer learning in action?

Sure! For instance, a model trained on a large dataset like ImageNet can be fine-tuned for a specific task like medical image classification.

What are the common architectures used for transfer learning?

Common architectures include VGG16, ResNet, and Inception, which are pre-trained on large datasets and can be adapted for new tasks.