Bibliographische Detailangaben
| 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 |
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