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…
Jean Pouget-Abadie
scientist · 2 mentions across 1 reading
In this course
Jean Pouget-Abadie is a co-author of the foundational 2014 paper introducing Generative Adversarial Networks (GANs), one of the most generative architectures shaping contemporary AI and synthetic media. His contribution to this work is central to the course's concern with how machine learning systems produce novel data and aesthetic artifacts, enabling both creative applications and the generative capacities that undergird modern synthetic culture. GANs represent a watershed moment in AI's ability to model and generate complex probability distributions across images, audio, and other domains—making this paper and its authors essential to understanding contemporary machine learning's creative and potentially destabilizing potential.
Mentioned in 1 reading
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