Translations:Batch Normalization Accelerating Deep Network Training/15/en: Difference between revisions

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Message definition (Batch Normalization Accelerating Deep Network Training)

During training, the mean and variance are computed per {{Term|mini-batch}}. During inference, batch statistics are replaced with '''population statistics''' — running averages accumulated during training — so that the output for a single sample is deterministic and does not depend on other samples in the batch.

During training, the mean and variance are computed per mini-batch. During inference, batch statistics are replaced with population statistics — running averages accumulated during training — so that the output for a single sample is deterministic and does not depend on other samples in the batch.

Revision as of 00:31, 27 April 2026 (view source) FuzzyBot (talk \| contribs) (Importing a new version from external source)		Latest revision as of 21:40, 27 April 2026 (view source) FuzzyBot (talk \| contribs) (Importing a new version from external source)
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	During training, the mean and variance are computed per mini-batch. During inference, batch statistics are replaced with '''population statistics''' — running averages accumulated during training — so that the output for a single sample is deterministic and does not depend on other samples in the batch.		During training, the mean and variance are computed per {{Term\|mini-batch}}. During inference, batch statistics are replaced with '''population statistics''' — running averages accumulated during training — so that the output for a single sample is deterministic and does not depend on other samples in the batch.