Translations:Batch Normalization Accelerating Deep Network Training/24/en: Difference between revisions
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While the original internal covariate shift explanation has been debated — subsequent work by Santurkar et al. (2018) argued that the primary benefit comes from smoothing the optimization landscape rather than reducing distributional shift — the practical effectiveness of batch normalization is undisputed. It was a key enabler of training the deep networks that drove progress in computer vision throughout the 2010s. | While the original internal covariate shift explanation has been debated — subsequent work by Santurkar et al. (2018) argued that the primary benefit comes from smoothing the optimization landscape rather than reducing distributional shift — the practical effectiveness of {{Term|batch normalization}} is undisputed. It was a key enabler of training the deep networks that drove progress in computer vision throughout the 2010s. | ||
Latest revision as of 21:40, 27 April 2026
While the original internal covariate shift explanation has been debated — subsequent work by Santurkar et al. (2018) argued that the primary benefit comes from smoothing the optimization landscape rather than reducing distributional shift — the practical effectiveness of batch normalization is undisputed. It was a key enabler of training the deep networks that drove progress in computer vision throughout the 2010s.