StyleGAN

Generative adversarial network StyleGAN uses style transfer techniques. It uses adaptive instance normalisation to generate images differently. StyleGAN trains on a dataset of real photos to create realistic-looking artificial images like faces. Its abilities enable high-quality synthesised visuals.

User objects:

– Graphic designers

– Digital artists

– Researchers in AI and machine learning

– Content creators

– Game developers

– Filmmakers for CGI

– Marketing professionals

– Data augmentation specialists

– Fashion designers for virtual modeling

– UI/UX designers for placeholder imagery.

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