Switchable Normalization

Introduced by Luo et al. in Differentiable Learning-to-Normalize via Switchable Normalization

Switchable Normalization combines three types of statistics estimated channel-wise, layer-wise, and minibatch-wise by using instance normalization, layer normalization, and batch normalization respectively. Switchable Normalization switches among them by learning their importance weights.

Source: Differentiable Learning-to-Normalize via Switchable Normalization

Latest Papers

PAPER DATE
Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
Guotai WangTao SongQiang DongMei CuiNing HuangShaoting Zhang
2020-07-07
Exemplar Normalization for Learning Deep Representation
Ruimao ZhangZhanglin PengLingyun WuZhen LiPing Luo
2020-03-19
Adapted Center and Scale Prediction: More Stable and More Accurate
| Wenhao Wang
2020-02-20
Switchable Normalization for Learning-to-Normalize Deep Representation
Ping LuoRuimao ZhangJiamin RenZhanglin PengJingyu Li
2019-07-22
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Ping LuoZhanglin PengJiamin RenRuimao Zhang
2018-11-19
Differentiable Learning-to-Normalize via Switchable Normalization
| Ping LuoJiamin RenZhanglin PengRuimao ZhangJingyu Li
2018-06-28

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