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Comment by jpmoyn | Hacker News Reader

This is really cool. My first thought of using it would be seeing how i look with a beard haha.

Can anyone explain how to do that (in theory).

I guess one needs to train a model from a dataset that contains a picture of oneself and many images of other people, some with beards (possibly CelebA-HQ). According to the README this would take about 2 weeks on a NVIDIA Tesla V100.

Given that we already have a model from CelebA-HQ would it be possible to use that as a pre-trained model and just train it a bit more with an additional image? If possible would that speed things up enough to do it on cheaper hardware in less than a day?

I'm definitely not an expert, but I think the way to go is to use an algorithm that can find a specific face in the latent space. If the latent space has enough dimensions (trained with a big enough set) it should already contain your face. The problem is to find it.

Actually I think this could do the job: https://arxiv.org/abs/1702.04782

https://github.com/simoroma/RecoverGANlatentVector

Thanks for the reply! I just want to add a little more details. As long as the training dataset is diverse enough, the trained GAN generator should be capable of generating images that are similar to the training dataset. Therefore there is no need to retrain the GAN. However, to "embed" a given face image to the latent space, we need to either use the optimization based method https://arxiv.org/abs/1702.04782 mentioned in "Geee"'s post, or train a encoding network. The encoding network would be much easier to train than the decoding/generator network because we do not need to use adversarial loss. I would love to include this functionality into my model once I got a job offer and have some spare time :)

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