Mixup

Mixup generates new synthetic training examples by linearly combining pairs of inputs and their corresponding labels. This can prevent the model from learning overly sharp class boundaries and may improve generalization, especially in noisy-label environments. It has been applied widely from image classification to some tabular scenarios. It remains one of the key modern data-driven regularization techniques.