Generative Adversarial Networks: A Case Study

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State-of-the-Art in the Architecture, Methods and Applications of StyleGAN

This state-of-the-art report covers the architecture and ways it has been employed since its conception, while also analyzing its severe limitations.

It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out.

We map out stylegan s impressive story through these investigations, and discuss the details that have made stylegan the go-to generator for image synthesis.

We further elaborate on the visual priors stylegan constructs, and discuss their use in downstream discriminative tasks.

Looking forward, we point out stylegan s limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.

Authors

Amit H. Bermano, Rinon Gal, Yuval Alaluf, Ron Mokady, Yotam Nitzan, Omer Tov, Or Patashnik, Daniel Cohen-Or

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