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arXiv · 1810.03764

Generalized Latent Variable Recovery for Generative Adversarial Networks

Nicholas Egan·Jeffrey Zhang·Kevin Shen
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Abstract

The Generator of a Generative Adversarial Network (GAN) is trained to transform latent vectors drawn from a prior distribution into realistic looking photos. These latent vectors have been shown to encode information about the content of their corresponding images. Projecting input images onto the latent space of a GAN is non-trivial, but previous work has successfully performed this task for latent spaces with a uniform prior. We extend these techniques to latent spaces with a Gaussian prior, and demonstrate our technique's effectiveness.

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Nicholas Egan, Jeffrey Zhang, Kevin Shen. 2018-10-09. Generalized Latent Variable Recovery for Generative Adversarial Networks. https://arxiv.org/abs/1810.03764

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