What are the primary advantages and limitations of using Generative Adversarial Networks (GANs) compared to other generative models?
Tuesday, 11 June 2024
by EITCA Academy
Generative Adversarial Networks (GANs) have emerged as a powerful class of generative models in the field of deep learning. Conceived by Ian Goodfellow and his colleagues in 2014, GANs have since revolutionized various applications, from image synthesis to data augmentation. Their architecture comprises two neural networks: a generator and a discriminator, which are trained simultaneously
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Advanced generative models, Modern latent variable models, Examination review
Tagged under:
Artificial Intelligence, Auto-Regressive Models, GANs, Image Synthesis, Normalizing Flows, VAEs