Advanced Deep Learning with Keras

Chapter 5
Auxiliary classifier GAN (ACGAN)
ACGAN is similar in principle to the Conditional GAN (CGAN) that we discussed
in the previous chapter. We're going to compare both CGAN and ACGAN. For both
CGAN and ACGAN, the generator inputs are noise and its label. The output is a fake
image belonging to the input class label. For CGAN, the inputs to the discriminator
are an image (fake or real) and its label. The output is the probability that the image
is real. For ACGAN, the input to the discriminator is an image, whilst the output is
the probability that the image is real and its class label. Following figure highlights
the difference between CGAN and ACGAN during generator training:
Figure 5.3.1: CGAN vs. ACGAN generator training.
The main difference is the input and output of the discriminator.
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