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 "Generative Adversarial Networks" paper alongside Ian Goodfellow and Yoshua Bengio, which introduced GANs as a framework for training generative models through adversarial competition between a generator and discriminator. In the course's focus on how machine learning architectures intersect with creative and artistic possibility, GANs represent a critical inflection point where neural networks became capable of synthesizing novel images and media rather than merely classifying or recognizing them. The paper's prominence in the syllabus signals how generative models have become central to contemporary debates about AI creativity, authenticity, and the aesthetics of algorithmic culture.
Mentioned in 1 reading
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