Label smoothing is a regularization technique used in classification tasks, particularly in deep learning models. It involves softening the target labels by assigning a small probability to all incorrect classes, rather than using hard labels (0 or 1). This approach helps prevent overfitting and encourages the model to be more uncertain about its predictions, which can lead to improved generalization on unseen data. Common use cases for label smoothing include image classification, natural language processing tasks, and any scenario where a model is prone to overconfident predictions.
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