About conditioning the discriminator

[r06922019]

Hi,

In Section 4 Implementation Details

Training details ...
We also find that not conditioning the discriminator D on input A leads to
better results (also discussed in [34]), ...

Does this means that the discriminator has no information to ensure the generated image to be conditioned on image A?
Say we are generating shoes from edges. Being unconditioned on the input image (edges), the discriminator should only be able to tell if the generated shoes are real/fake, but not able to tell if the generated shoes doesn't match the conditions (edge)?
Or some other mechanism is working on the condition part?

Looking forward to your reply, thanks! Nice work!

[junyanz]

We also have the L1 image reconstruction to enforce the correspondence.

[r06922019]

I see. Thanks for your reply.