Discriminator of a Conditional GAN with continuous labels OK, let's say we have well-labeled images with non-discrete labels such as brightness or size or something and we want to generate images based on it.  If it were done with a discrete label it could be done like:

def forward(self, inputs, label):
    self.batch = inputs.size(0)
    h = self.res1(inputs)
    h = self.attn(h)

        ...

    h = self.res5(h)
    h = torch.sum((F.leaky_relu(h,0.2)).view(self.batch,-1,4*4), dim=2)
    outputs = self.fc(h)

    if label is not None:
        embed = self.embedding(label)
        outputs += torch.sum(embed*h,dim=1,keepdim=True)


The embedding can be made to match any shape to be added to a hidden layer and summing the embeddings to the latent, which forces the discriminator to recognize the class that's it's discriminating to make a better judgment on the discrimination.  That's cool and all, but this method uses embeddings which is the discrete method.  How about a continuous label?  I can't really find a method for doing this except for some semisupervised methods whereas I have exact labels.  Is anyone able to help me?
I think the answer is, you have to either create a very large number of categories, or do it completely differently than with the code I provided.  The paper as a good solution to the problem.