That makes me wonder.
Let’s say the input sample is $ x $.
So we feed the net with $ \frac{x - m}{v} = \frac{x}{v} - \frac{m}{v} $.
Now, if the first layer of the net is Linear Layer we have the Bias term (Assuming it is turned on).
If there is need to subtract a value from all samples, like $ \frac{m}{v} $, I’d assume it will be learned.
So I see the point in dividing, but what’s the point in centering?