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In batch normalization, mean and standard deviation are calculated feature wise and normalization step is done instance wise and in layer normalization mean and standard deviation are calculated instance wise nd normalization step is done feature wise; is this right or not?
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What is the use of having “batches” in batch normalization? Do we feed the network second batch after first pass is done in neural network?
I could not find any good resources on this and definitions seems too tricky to understand.
yes you are right . from my knowledge In layer normalization, mean and standard deviation are computed per instance across all features, and normalization is performed per feature using these calculated values.
The term “batch” in batch normalization refers to the normalization being conducted across a batch of data during training, enhancing the stability of the training process. However, during inference or testing, a single instance can be normalized without needing a batch. In these cases, mean and standard deviation computed during training are often used for normalization.