Comparison of Batch, Layer, Instance and Group Normalization

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* Comparison of Batch Normalization, Layer Normalization, Instance Normalization and Group Normalization on a Convolutional Layer output
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References
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe, Christian Szegedy
arxiv.org/abs/1502.03167
Layer Normalization
Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
arxiv.org/abs/1607.06450
Instance Normalization: The Missing Ingredient for Fast Stylization
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
arxiv.org/abs/1607.08022
Group Normalization
Yuxin Wu, Kaiming He
arxiv.org/abs/1803.08494
#machinelearning #computervision
#layernormalization #deeplearning #ai
#batchnormalization #groupnormalization
#instancenormalization
#artificialintelligence #aitutorial #education
#convolutionalneuralnetwork #neuralnetwork
#convolutionalneuralnetworks #neuralnetworks
#imageprocessing #datascience
#computervisionwithhuseyinozdemir

[@lbognini]

The rows and columns labels are confusing.
"Feature map i" should be "Data point i"
"Filter j" should be "Feature (map) j"

[@huseyin_ozdemir]

Figure shows the output of a convolutional layer, not input. And this is written next to the figure.
Because layer normalization works on the output of previous layer, it normalizes the output of previous layer.
The row labelled as "Feature map i" shows the convolution results of all filters with input i of batch.