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Closed-Form Factorization of Latent Semantics in GANs
[article]
2021
arXiv
pre-print
A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear classifiers in the latent space. However, they require a clear definition of the target attribute as well as the corresponding manual annotations, limiting their applications in
arXiv:2007.06600v4
fatcat:y2jtbcp345gsvad6dec7lbv6ou