What is Lovasz loss and where is it applied?

Lovasz loss is a loss function based on the Lovász extension, used for optimizing segmentation quality metrics such as Intersection over Union (IoU). Unlike traditional loss functions (e.g., cross-entropy), Lovasz loss directly optimizes the IoU metric, which is especially useful in semantic segmentation tasks.

It is applied in computer vision to train neural networks that perform image segmentation tasks, where maximizing the overlap accuracy between predicted and true object masks is important.

An example of its use: when training a segmentation model for medical images, where accurately highlighting the affected area is critical, Lovasz loss helps improve mask quality compared to classical loss functions.