@article{generativeadversarialnetworks2014,
  title = {Generative Adversarial Networks (GAN)},
  author = {Goodfellow, I. and Pouget-Abadie, J. and Mirza, M. and Xu, B. and Warde-Farley, D. and Ozair, S. and Courville, A. and Bengio, Y.},
  year = {2014},
  journal = {NeurIPS 2014},
  url = {https://arxiv.org/abs/1406.2661},
  abstract = {Goodfellow et al. (2014) introduced Generative Adversarial Networks, a training framework in which two neural networks compete: a generator attempts to produce realistic samples and a discriminator attempts to distinguish them from real data. The adversarial dynamic drives both networks to improve until the generator produces outputs indistinguishable from training data. The GAN framework is the named origin of StyleGAN, pix2pix and CycleGAN, making it prerequisite knowledge for any course covering generative image synthesis. Understanding GANs is foundational for animation educators introducing AI image generation, as it underpins the entire pre-diffusion era of generative tools used in character, concept and visual-development workflows.},
  keywords = {generative-ai, image-generation},
  note = {AI \& Animation Education Knowledge Base}
}