If a GAN generator has the same (but reversed) hidden layer architecture as the discriminator, is a the discriminator's loss expected to be approximately double the generator's?
In the examples I'm learning from, the:
- Discriminator loss is the sum of a batch of real and a batch fake images losses.
- Generator loss is from only one batch of image losses
All batch sizes are the same.
Intuitively, there are twice as many examples in the discriminator loss, so I'd expect it to be twice as large.
Is there any reason not to scale the discriminator loss by dividing it by $2$?
Are batch sizes used in the loss functions usually all the same size?