Normalization layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.
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Jixipix Spectral Art 1 1 44 Resz
Jixipix Spectral Art 1 1 44 Inch
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| METHOD | YEAR | PAPERS |
|---|---|---|
Layer Normalization | 2016 | 2797 |
| 2015 | 2239 | |
Local Response Normalization | 2012 | 362 |
| 2016 | 190 | |
Spectral Normalization | 2018 | 80 |
| 2017 | 60 | |
Weight Normalization | 2016 | 47 |
| 2017 | 44 | |
Group Normalization | 2018 | 28 |
| 2018 | 20 | |
Weight Demodulation | 2019 | 13 |
| 2018 | 6 | |
Weight Standardization | 2019 | 4 |
| 2009 | 4 | |
Conditional Instance Normalization | 2016 | 3 |
| 2018 | 3 | |
Attentive Normalization | 2019 | 2 |
| 2018 | 2 | |
Filter Response Normalization | 2019 | 2 |
| 2017 | 2 | |
Cosine Normalization | 2017 | 1 |
| 2019 | 1 | |
Instance-Level Meta Normalization | 2019 | 1 |
| 2018 | 1 | |
Mode Normalization | 2018 | 1 |
| 2019 | 1 | |
Virtual Batch Normalization | 2016 | 1 |
| 2019 | 1 | |
Online Normalization | 2019 | 1 |
