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.
    * '''Batch normalization''' — normalising layer inputs reduces internal covariate shift and has a mild regularizing effect.
    * '''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.
    * '''Label smoothing''' — replaces 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.

    Revision as of 22:02, 27 April 2026

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    Message definition (Overfitting and Regularization)
    * '''{{Term|batch normalization}}''' — normalising layer inputs reduces internal covariate shift and has a mild regularizing effect.
    * '''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.
    • 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.
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