C Mixup

C-Mixup is a data augmentation technique enhancing the generalization of deep learning models, particularly in regression tasks and scenarios with limited data or noisy labels. Research focuses on adapting C-Mixup for various applications, including continual learning, where it addresses task confusion, and graph neural networks, where it incorporates structural information. By selectively interpolating data points based on label similarity, C-Mixup improves model robustness and performance, impacting both the accuracy and generalizability of machine learning models across diverse domains.