What is transfer learning in segmentation?

What is transfer learning in segmentation?

Transfer learning in segmentation involves using a pre-trained model on a new segmentation task to improve performance.

How does it improve performance compared to training from scratch?

It leverages learned features from a large dataset, reducing the need for extensive labeled data and speeding up training.

Can you give an example of a model used for this?

Sure! Models like U-Net and Mask R-CNN are commonly used for segmentation tasks with transfer learning.

What are the challenges of using transfer learning in segmentation?

Challenges include domain shift, where the source and target datasets differ significantly, affecting model performance.