How does label smoothing modify cross-entropy cost functions?

Label smoothing prevents overconfidence by softening target distributions.

Instead of using one-hot labels, label smoothing distributes probability mass across all classes. This modifies cross-entropy cost, reducing overconfidence and improving generalization. It also improves calibration and reduces overfitting in deep classification models.