Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

The latent spaces of GAN models often have semantically meaningful directions. Moving in these directions corresponds to human-interpretable image transformations, such as zooming or recoloring, enabling a more controllable generation process. However, the discovery of such directions is currently p...

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Bibliographische Detailangaben

Hauptverfasser:	Voynov, Andrey, Babenko, Artem
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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