Incorporating labels in ACGAN: Does torch.mul() have the same effect as concatenation and adding as additional channels?

[ghost]

Hi, first of all, thanks for your amazing work!

I have a question regarding a difference between your implementation and a recommendation from the repository "ganhacks" that you referred to. In their repository they state to use an embedding layer for labels, and add as additional channels to images when implementing the conditional gan. I have often seen this done by concatenating the embedding and the noise vectors. However, in your code you use a multiplication, as below;

label_embedding = self.embedding(label)
x = torch.mul(noise,label_embedding)
x = x.view(-1,100,1,1)

Is this the same as the concatenation and if not, what is the difference and would you prefer one over the other?

Thank you in advance!