Training Techniques
Label smoothing
Label smoothing is a regularization technique used in training machine learning models, especially neural networks, to prevent overfitting and improve generalization. It works by replacing hard labels (e.g., [0, 1, 0] for a three-class classification) with soft labels, which are a mixture of the hard target and a uniform distribution over all classes.
Explanation
In traditional classification tasks, models are trained to predict a specific class with a probability of 1 (the 'hard label') and all other classes with a probability of 0. This can lead to overconfidence in the model's predictions and poor generalization to unseen data. Label smoothing mitigates this issue by softening the target probabilities. Instead of aiming for a perfect 1.0 probability for the correct class, label smoothing introduces a small probability (epsilon) for the other classes. For example, with a smoothing factor of ε, the target probability for the correct class becomes 1 - ε, and the remaining probability ε is distributed evenly among the other classes. This encourages the model to be less certain and to explore other possibilities, thereby improving robustness and reducing overfitting. The effect is that the model learns to be less confident in its predictions, which can lead to better calibration and improved performance on unseen data. Label smoothing is particularly effective when dealing with noisy labels or complex datasets where the true underlying distribution is uncertain.