Focal Loss

Focal loss is a loss function designed to address class imbalance in deep learning models, particularly beneficial for tasks with many easy examples and few hard ones. Current research focuses on refining focal loss for improved calibration and robustness, often integrating it with various architectures like U-Nets, transformers, and RetinaFace for applications such as medical image segmentation, object detection in satellite imagery and video, and road asset detection. These advancements enhance the accuracy and reliability of deep learning models across diverse fields, leading to improved performance in tasks ranging from medical diagnosis to autonomous driving.