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Latent Space Conditioning on Generative Adversarial Networks

  • Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback ofGenerative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stability. Nevertheless, the main drawback of such models is the necessity of annotated data. In this work, we introduce an novel framework that benefits from two popular learning techniques, adversarial training and representation learning, and takes a step towards unsupervised conditional GANs. In particular, our approach exploits the structure of a latent space (learned by the representation learning) and employs it to condition the generative model. In this way, we break the traditional dependency between condition and label, substituting the latter by unsupervised features coming from the latent space. Finally, we show that this new technique is able to produce samples on demand keeping the quality of its supervised counterpart.show moreshow less

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Metadaten
Author:Ricard Durall Lopez, Kalun Ho, Franz-Josef Pfreundt, Janis KeuperORCiDGND
Date of Publication (online):2020/12/16
Page Number:12
Language:English
Document Type:Article (unreviewed)
Open Access:Frei zugänglich
Institutes:Bibliografie
Release Date:2021/01/25
Licence (German):License LogoEs gilt das UrhG
Note:
Pre-Print
URL:https://www.researchgate.net/publication/347398434_Latent_Space_Conditioning_on_Generative_Adversarial_Networks
ArXiv Id:http://arxiv.org/abs/2012.08803