After digging in the code for a few hours I discovered how zi2zi utilizes the pix2pix methodology. If I am correct, the data is split into two parts: real_A
and real_B
. real_A
is fed into the generator along with the class label embedding_ids
and produces fake_b
. The discriminator then aims at discriminating a fake_b
and real_b
with real_a
as the target image.
Conclusively, this seemingly works like an autoencoder, but with the discriminator as an evaluation metric. In concept, there isn't much that is a difference between pix2pix and other GANs with encoders.
与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…