2022
DOI: 10.48550/arxiv.2206.00048
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PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs

Abstract: Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering global semantic directions that do not facilitate localized control, or require some form of supervision through manually provided regions or segmentation ma… Show more

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Cited by 2 publications
(1 citation statement)
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“…Another line of work leverages the power of Generative Adversarial Networks (GANs) [14], which have recently been used for discovering controllable generation paths in their latent or feature spaces [2,34,35,43,44]. Towards face anonymization, GANs have been incorporated in order to synthesize new images in order to obtain photos that maintain most of the image while changing the face of the subject of interest.…”
Section: Ciagan Deepprivacy Oursmentioning
confidence: 99%
“…Another line of work leverages the power of Generative Adversarial Networks (GANs) [14], which have recently been used for discovering controllable generation paths in their latent or feature spaces [2,34,35,43,44]. Towards face anonymization, GANs have been incorporated in order to synthesize new images in order to obtain photos that maintain most of the image while changing the face of the subject of interest.…”
Section: Ciagan Deepprivacy Oursmentioning
confidence: 99%