TY  - JOUR
TI  - Generative Adversarial Networks (GAN)
AU  - Goodfellow, I.
AU  - Pouget-Abadie, J.
AU  - Mirza, M.
AU  - Xu, B.
AU  - Warde-Farley, D.
AU  - Ozair, S.
AU  - Courville, A.
AU  - Bengio, Y.
PY  - 2014
DA  - 2014/06//
JO  - NeurIPS 2014
AB  - 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.
KW  - generative-ai
KW  - image-generation
UR  - https://arxiv.org/abs/1406.2661
LA  - en
ER  -