Analyzing and Improving the Image Quality of StyleGAN 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Gespeichert in:
| Titel: | Analyzing and Improving the Image Quality of StyleGAN 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
|---|---|
| Autoren: | Karras, T., Laine, Samuli, Aittala, M., Hellsten, J., Lehtinen, J., Aila |
| Quelle: | IEEE Computer Society Conference on Computer Vision and Pattern Recognition. :8107-8116 |
| Verlagsinformationen: | 2020. |
| Publikationsjahr: | 2020 |
| Schlagwörter: | ta113, Modulation, Measurement, Standards, unconditional image modeling, data visualisation, generator normalization, style-based GAN architecture, data-driven unconditional generative image modeling, distribution quality metrics, unsupervised learning, Convolution, Generators, StyleGAN architecture, perceived image quality, Image resolution, Training, neural net architecture, image coding, image resolution |
| Publikationsart: | Conference object |
| Sprache: | English |
| ISSN: | 1063-6919 |
| DOI: | 10.1109/cvpr42600.2020.00813 |
| Zugangs-URL: | http://juuli.fi/Record/0371030520 |
| Dokumentencode: | edsair.CSC...........3749ed8e55b44f3ac890d3d58a3cd027 |
| Datenbank: | OpenAIRE |
Full Text Finder 
Nájsť tento článok vo Web of Science Schreiben Sie den ersten Kommentar!