Thx for reply @smth . So, to sum up,

for a m dimension 1-D vector input

if the input contains m attributes (e.g. a person’s weight, name, gender, etc.), it should be treated as m  channels and one  feature, and, do channel-wise batch-norm.
if the input contains 1 attribute but have m values (e.g. a piece of voice signal with m time steps ), then it is better to treat it as one  channel and m features.


for a mxn 2-D matrix input

if the input contains mxn features (say, re-arrange a 1-D “person information” vector mentioned above into 2-D matrix ), likewise, we should treat the input as mxn channels and one feature

if the input contains 1 attribute(e.g. the “red” channel of an 2-D image), should be treated as 1 channel and mxn features




Feel free to correct me if I was wrong.