Dog StyleGAN2-ADA

Progress in GANs has enabled the generation of high-res-olution photorealistic images of astonishing quality. StyleGANs allow for compelling attribute modification on such images via mathematical operations on the latent style vectors in the W/W+ space that effectively modulate the rich hierarchical representations of the generator.

Data and Resources

Cite this as

Tejan Karmali, Rishubh Parihar, Susmit Agrawal, Harsh Rangwani, Varun Jampani, Maneesh Singh, R. Venkatesh Babu (2024). Dataset: Dog StyleGAN2-ADA. https://doi.org/10.57702/v0rebhfu

DOI retrieved: December 2, 2024

Additional Info

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Created December 2, 2024
Last update December 2, 2024
Author Tejan Karmali
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Rishubh Parihar
Susmit Agrawal
Harsh Rangwani
Varun Jampani
Maneesh Singh
R. Venkatesh Babu
Homepage https://github.com/tejan-karmali/Dog-StyleGAN2-ADA