Class Loss

Class loss functions aim to improve the performance of machine learning models, particularly in scenarios with imbalanced datasets or complex class structures. Current research focuses on developing and adapting class-balanced loss functions for various model architectures, including gradient boosting decision trees and deep learning models used in image segmentation and face recognition. These advancements address challenges like skewed class distributions and limited labeled data, leading to more robust and accurate models across diverse applications such as medical image analysis and weed detection. The impact extends to improving the efficiency and generalizability of machine learning in real-world settings.