What are the primary advantages and limitations of using Generative Adversarial Networks (GANs) compared to other generative models?
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
How do modern latent variable models like invertible models (normalizing flows) balance between expressiveness and tractability in generative modeling?
Modern latent variable models, such as invertible models or normalizing flows, are instrumental in the landscape of generative modeling due to their unique ability to balance expressiveness and tractability. This balance is achieved through a combination of mathematical rigor and innovative architectural design, which allows for the precise modeling of complex data distributions while maintaining
How does variational inference facilitate the training of intractable models, and what are the main challenges associated with it?
Variational inference has emerged as a powerful technique for facilitating the training of intractable models, particularly in the domain of modern latent variable models. This approach addresses the challenge of computing posterior distributions, which are often intractable due to the complexity of the models involved. Variational inference transforms the problem into an optimization task, making
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Advanced generative models, Modern latent variable models, Examination review