Generative Adversarial Nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair†, Aaron Courville, Yoshua Bengio‡
Département d’informatique et de recherche opérationnelle
Université de Montréal
M…The promise of deep learning is to discover rich, hierarchical models [2] that represent probability distributions over the kinds of data encountered in artificial intelligence applications, such as natural images, audio waveforms containin…
Sherjil Ozair
scientist · 2 mentions across 1 reading
In this course
Sherjil Ozair is a co-author of the foundational "Generative Adversarial Networks" paper, one of the most influential architectures in modern machine learning that enables the creation of synthetic data through competition between generator and discriminator networks. His contribution to GANs is central to the course's exploration of how machines learn to produce novel outputs and manipulate representation, making him essential to understanding contemporary generative AI's technical and cultural implications. Though Ozair appears primarily as a co-author rather than a theorized figure, his work on GANs directly supports the seminar's investigation of algorithmic creativity and the systems through which AI learns to model and simulate reality.
Mentioned in 1 reading
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