Description: Label smoothing is a technique used in the training of machine learning models, including convolutional neural networks (CNNs), that aims to mitigate the problem of overfitting and improve the model’s generalization. This technique involves modifying the labels of the target classes so that instead of assigning a binary value (0 or 1) to each class, values are assigned that represent a probability distribution. For example, instead of having a label of 1 for one class and 0 for the others, a vector can be used that assigns a value of 0.9 to the correct class and 0.1 to the incorrect ones. This prevents the model from becoming overly confident in its predictions, which can lead to poor performance on unseen data. Label smoothing also helps to handle noise in the training data, allowing the model to learn in a more robust and flexible manner. This technique has become particularly relevant in various machine learning tasks, including image classification and natural language processing, where data ambiguity and variability can be significant. In summary, label smoothing is an effective strategy for improving the quality and reliability of predictions made by deep learning models, promoting better adaptation to real-world situations.
