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The recent advances in generative modeling have gained a lot of attention both from ML researchers, practitioners, and businesses. Currently, the most attractive results are obtained using generative adversarial networks (GANs). Since their rise in 2014, GANs are being thoroughly studied from multiple viewpoints, e.g. probabilistic and game-theoretic ones.

This talk will survey several fundamental works related to the optimization objective of such models, and discuss the recently proposed strategies of their training.

Lecture slides: https://www.reddit.com/r/2D3DAI/comments/lzwffx/lecture_references_a_survey_on_generative/

The talk is based on the papers:
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization [NeurIPS 2016] Sebastian Nowozin, Botond Cseke, and Ryota Tomioka.
https://arxiv.org/abs/1606.00709

Which Training Methods for GANs do actually Converge? [ICML 2018] Lars Mescheder, Andreas Geiger, and Sebastian Nowozin.
Arxiv: https://arxiv.org/abs/1801.04406
git: https://github.com/LMescheder/GAN_stability

Training generative adversarial networks with limited data [NeurIPS 2020] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila.
arxiv: https://arxiv.org/abs/2006.06676v2
git: https://github.com/NVlabs/stylegan2-ada

Presenter BIO:

Denis Korzhenkov is a researcher at Samsung AI Center in Moscow. His work is mainly focused on the generative modeling of images. Denis serves as a reviewer at ICLR, CVPR, and ICCV. Got a MS in Mathematics at Lomonosov Moscow State University.

This disclaimer informs readers that the views, thoughts, and opinions expressed in the talk belong solely to the author, and not necessarily to the author's employer, organization, committee, or other group or individual.

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