What I know so far in DCGAN is that a discriminator is trained using the labeled data (so maybe that occurs before training the generative model). Also, I know that there is race between the generator and the discriminator, so maybe training occur online. So I have some concerns here:
- How many outputs the discriminator should have (Is it one output that describes the probability, ex: P(x))?
- How do we chose its output when feeding fake data vs. real data?
- Is the discriminator trained before using it with the DCGAN or the training is done online (It is mentioned in the Original Paper: Generative Adversarial Nets https://arxiv.org/pdf/1406.2661.pdf, that the whole network is trained using the back propagation), hence I think its online?
Any help is much appreciated!!