Precise Recovery of Latent Vectors from Generative Adversarial Networks · HackerPapers discussions

Precise Recovery of Latent Vectors from Generative Adversarial Networks — mentioned by summitkwan

Mon, 29 Oct 2018 08:21:01 GMT

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…

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Precise Recovery of Latent Vectors from Generative Adversarial Networks — mentioned by Geee

Fri, 26 Oct 2018 22:56:46 GMT

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

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Precise Recovery of Latent Vectors from Generative Adversarial Networks — HN submission

Sun, 19 Feb 2017 12:30:21 GMT

Reverting Generative adversarial networks mapping

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.