What Label Smoothing Meaning, Applications & Example
Technique to improve model generalization by softening target labels.
What is Label Smoothing?
Label Smoothing is a regularization technique used in classification tasks to prevent the model from becoming overly confident in its predictions. It softens the hard targets by assigning a small probability to all other classes, instead of giving a probability of 1 to the correct class. This technique helps improve generalization and prevents overfitting .
How Label Smoothing Works
Label Smoothing works by adjusting the target labels in the training process:
- The correct class is given a probability slightly less than 1 (e.g., 0.9).
- The remaining probability (e.g., 0.1) is evenly distributed among the incorrect classes.
Applications of Label Smoothing
- Neural Networks: Helps improve the generalization ability of models by making them less likely to overfit on the training data.
- Image Classification: Applied in tasks like object recognition where precise confidence in class predictions can lead to overfitting.
- Natural Language Processing (NLP) : Used in tasks like machine translation to avoid overly confident predictions.
Example of Label Smoothing
In a classification task with three classes, if the true label is class 1, label smoothing could modify the target label from [1, 0, 0] to something like [0.9, 0.05, 0.05], softening the output probabilities and making the model less likely to overfit.