Class Aware Regularization

Class-aware regularization techniques aim to improve the performance of semantic segmentation models, particularly in scenarios with class imbalance, by explicitly incorporating class-level information into the learning process. Current research focuses on developing novel loss functions that encourage compact intra-class representations and maximize inter-class distances, often leveraging clustering to create balanced subclasses for more robust training. These methods demonstrate significant improvements in accuracy and generalization across various benchmark datasets, offering a valuable tool for enhancing the robustness and efficiency of semantic segmentation in diverse applications, such as medical image analysis.