Label Smoothing

Label smoothing is a regularization technique that improves the generalization and calibration of deep learning models by softening one-hot encoded labels during training. Current research explores its application across diverse model architectures and tasks, including image classification, natural language processing, and time series analysis, often in conjunction with other regularization methods like dropout or data augmentation. This technique's effectiveness in mitigating overfitting, enhancing robustness to noisy data, and improving model confidence estimations has significant implications for improving the reliability and performance of machine learning systems in various applications.