Label Smoothing is a technique used in the field of Machine Learning to regularize the learning process by developing predictions near the previously observed labels instead of exclusively relying on over-confident predictions. The technique reduces the effect of over-confidence in predictions by introducing uniformity into the probability distribution of the labels. The primary goal of label smoothing is to reduce overfitting of the model and to improve the generalization of the predictions.
Label smoothing is often used with neural network models. It provides a regularizing effect by decreasing the sharpness of decisions and pushing predictions away from confident extremes. This is done by replacing the hard label (e.g. 0 or 1) with a probability distribution that has some degree of continuity. For example, in a binary classification task, a label of 1 would be replaced with a probability of 0.9 to indicate near-certainty.
Label smoothing may also be used to alleviate the effect of the infamous long-tailed distributions that tend to exist in some datasets. By smoothing the labels, the confident predictions are restricted and sharp edges between the classes are blurred.
Label smoothing can be effective in improving the generalization capabilities of a model, since it reduces over-confidence in predictions and penalizes confident predictions that might be incorrect. It should be noted, however, that label smoothing does not guarantee improved accuracy as it is dependent on the datasets and the problem at hand.
Overall, label smoothing is a regularization technique that is used to improve the generalization capabilities of a model by reducing over-confidence in predictions. It can be used with neural network models to reduce the effect of over-confident predictions and to reduce the effect of long-tailed distributions that exist in some datasets.