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Hi, thanks for the awesome paper. I have some questions regarding the networks in the paper.
Is there only a single generator covering both domain A and B? How is it supposed to generate both domains' features? (For discriminator it has two branches so it makes sense.)
Thanks,
The text was updated successfully, but these errors were encountered:
Yes, the weights of the generator are shared between A-->B and B--> A. According to our observation, such single generator can generate high-quality results, if loss functions are properly designed. Many recent works such as FUNIT and stargan also only use single generator for two/multiple domains.
Hi, thanks for the awesome paper. I have some questions regarding the networks in the paper.
Is there only a single generator covering both domain A and B? How is it supposed to generate both domains' features? (For discriminator it has two branches so it makes sense.)
Thanks,
The text was updated successfully, but these errors were encountered: