Translations:Overfitting and Regularization/34/en: Difference between revisions
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* ''' | * '''{{Term|batch normalization}}''' — normalising layer inputs reduces internal covariate shift and has a mild regularizing effect. | ||
* '''Label smoothing''' — replaces one-hot targets with a mixture, e.g. <math>y_{\text{smooth}} = (1 - \epsilon)\, y + \epsilon / C</math>, preventing overconfidence. | * '''Label smoothing''' — replaces {{Term|one-hot encoding|one-hot}} targets with a mixture, e.g. <math>y_{\text{smooth}} = (1 - \epsilon)\, y + \epsilon / C</math>, preventing overconfidence. | ||
* '''Noise injection''' — adding Gaussian noise to inputs, weights, or gradients during training. | * '''Noise injection''' — adding Gaussian noise to inputs, weights, or gradients during training. | ||
Latest revision as of 23:34, 27 April 2026
- batch normalization — normalising layer inputs reduces internal covariate shift and has a mild regularizing effect.
- Label smoothing — replaces one-hot targets with a mixture, e.g. $ y_{\text{smooth}} = (1 - \epsilon)\, y + \epsilon / C $, preventing overconfidence.
- Noise injection — adding Gaussian noise to inputs, weights, or gradients during training.