It does, and for the exact same reason than a plan filled with 1.
You have a lot of bias inside your networks so whatever the input you
give, you can be sure it will be transformed, be it a plan full of 0 or
a plan full of 1. As you said, it helps the network to keep the track of
the boundaries after the image is zero-padded. The real question is more
like: is it useful to have both?
I haven't tested it but I guess that the min-max boundaries has to be
somehow a useful information for the network.
Vincent Richard
Le 18-Jul-17 à 7:53 PM, Brian Lee a écrit :
I've been wondering about something I've seen in a few papers
(AlphaGo's paper, Cazenave's resnet policy architecture), which is the
presence of an input plane filled with 0s.
The input features also typically include a plane of 1s, which makes
sense to me - zero-padding before a convolution means that the 0/1
demarcation line tells the CNN where the edge of the board is. But as
far as I can tell, a plane of constant 0s should do absolutely
nothing. Can anyone enlighten me?
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