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?