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Latent Space Conditioning on Generative Adversarial Networks

 
: Durall, Ricard; Ho, Kalun; Pfreundt, Franz-Josef; Keuper, Janis

:

Farinella, Giovanni Maria (Ed.) ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2021. Proceedings. Vol.4: VISAPP : February 8-10, 2021
Setúbal: SciTePress, 2021
ISBN: 978-989-758-488-6
pp.24-34
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <16, 2021, Online>
International Conference on Computer Vision Theory and Applications (VISAPP) <16, 2021, Online>
English
Conference Paper
Fraunhofer ITWM ()

Abstract
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 Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data.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 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.

: http://publica.fraunhofer.de/documents/N-638060.html