Projekt 1: simple fully connected GAN
Projekt 2: optimized fully connected GAN - in processing
Projekt 3: build up a framework for comparison - pending

• evaluation per FID (Fréchet Inception Distance)
• normalization via calculated mean and std
• examples of fake images

Guidance paper

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2020).
Generative adversarial networks. Communications of the ACM, 63(11), 139-144.
link

Radford, A., Metz, L., & Chintala, S. (2015).
Unsupervised representation learning with deep convolutional generative adversarial networks.
link

Wang, Y. (2020).
A mathematical introduction to generative adversarial nets (GAN).
link

Wang, Z., She, Q., & Ward, T. E. (2021).
Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR), 54(2), 1-38.
link

(evaluation method)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017).
Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30.
link