Titlebook: Normalization Techniques in Deep Learning; Lei Huang Book 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to

Nonchalant

書目名稱Normalization Techniques in Deep Learning影響因子(影響力)




書目名稱Normalization Techniques in Deep Learning影響因子(影響力)學科排名




書目名稱Normalization Techniques in Deep Learning網絡公開度




書目名稱Normalization Techniques in Deep Learning網絡公開度學科排名




書目名稱Normalization Techniques in Deep Learning被引頻次




書目名稱Normalization Techniques in Deep Learning被引頻次學科排名




書目名稱Normalization Techniques in Deep Learning年度引用




書目名稱Normalization Techniques in Deep Learning年度引用學科排名




書目名稱Normalization Techniques in Deep Learning讀者反饋




書目名稱Normalization Techniques in Deep Learning讀者反饋學科排名

Flirtatious

,Motivation and?Overview of?Normalization in?DNNs,een examples will be dominated by these dimensions, which will impair the performance of the learner. Besides, normalizing an input can improve the optimization efficiency for parametric models. There exist theoretical advantages to normalization for linear models, as we will illustrate.

Blatant

,A General View of?Normalizing Activations,oduce the preliminary work of normalizing activations of DNNs, prior to the milestone normalization technique—batch normalization (BN)?[.]. We then illustrate the algorithm of BN and how it is developed by exploiting the merits of the previous methods.

bleach

,BN for?More Robust Estimation,ng along the batch dimension, as introduced in previous sections. Here, we will discuss the more robust estimation methods that also address this problem of BN. One way to reduce the discrepancy between training and inference is to combine the estimated population statistics for normalization during training.

語言學

ASSAY

過于平凡

,Summary and?Discussion,le to design new normalization methods tailored to specific tasks (by the choice of NAP) or improve the trade-off between efficiency and performance (by the choice of NOP). We leave the following open problems for discussion.

AVANT

詞匯表

,Multi-mode and?Combinational Normalization, GMM distribution as: . where . represents .-th Gaussian in the mixture model .. It is possible to estimate the mixture coefficient . and further derive the soft-assignment mechanism ., by using the expectation-maximization (EM)?[.] algorithm.

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