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We study the effect of the stochastic gradient noise on the training of generative adversarial networks (GANs) and show that it can prevent the convergence of standard game optimization methods, while the batch version converges. We address this issue with a novel stochastic variance-reduced extragradient (SVRE) optimization algorithm, which for a large class of games improves upon the previous convergence rates proposed in the literature. We observe empirically that SVRE performs similarly to a batch method on MNIST while being computationally cheaper, and that SVRE yields more stable GAN training on standard datasets.
Author Information
Tatjana Chavdarova (DeepMind & Mila & Idiap & EPFL)
Gauthier Gidel (Mila)
I am a Ph.D student supervised by Simon Lacoste-Julien, I graduated from ENS Ulm and Université Paris-Saclay. I was a visiting PhD student at Sierra. I also worked for 6 months as a freelance Data Scientist for Monsieur Drive (Acquired by Criteo) and I recently co-founded a startup called Krypto. I'm currently pursuing my PhD at Mila. My work focuses on optimization applied to machine learning. More details can be found in my resume. My research is to develop new optimization algorithms and understand the role of optimization in the learning procedure, in short, learn faster and better. I identify to the field of machine learning (NIPS, ICML, AISTATS and ICLR) and optimization (SIAM OP)
François Fleuret (Idiap)
François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006. He is Full Professor in the department of Computer Science at the University of Geneva, and Adjunct Professor in the School of Engineering of the École Polytechnique Fédérale de Lausanne. He has published more than 80 papers in peer-reviewed international conferences and journals. He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, serves as Area Chair for NeurIPS, AAAI, and ICCV, and in the program committee of many top-tier international conferences in machine learning and computer vision. He was or is expert for multiple funding agencies. He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the development and commercialization of deep learning solutions for engineering design. His main research interest is machine learning, with a particular focus on computational aspects and sample efficiency.
Simon Lacoste-Julien (Mila, Université de Montréal & SAIL Montreal)
Simon Lacoste-Julien is an associate professor at Mila and DIRO from Université de Montréal, and Canada CIFAR AI Chair holder. He also heads part time the SAIT AI Lab Montreal from Samsung. His research interests are machine learning and applied math, with applications in related fields like computer vision and natural language processing. He obtained a B.Sc. in math., physics and computer science from McGill, a PhD in computer science from UC Berkeley and a post-doc from the University of Cambridge. He spent a few years as a research faculty at INRIA and École normale supérieure in Paris before coming back to his roots in Montreal in 2016 to answer the call from Yoshua Bengio in growing the Montreal AI ecosystem.
More from the Same Authors
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2020 Poster: Adversarial Example Games »
Joey Bose · Gauthier Gidel · Hugo Berard · Andre Cianflone · Pascal Vincent · Simon Lacoste-Julien · Will Hamilton -
2020 Poster: Differentiable Causal Discovery from Interventional Data »
Philippe Brouillard · Sébastien Lachapelle · Alexandre Lacoste · Simon Lacoste-Julien · Alexandre Drouin -
2020 Spotlight: Differentiable Causal Discovery from Interventional Data »
Philippe Brouillard · Sébastien Lachapelle · Alexandre Lacoste · Simon Lacoste-Julien · Alexandre Drouin -
2020 Poster: Real World Games Look Like Spinning Tops »
Wojciech Czarnecki · Gauthier Gidel · Brendan Tracey · Karl Tuyls · Shayegan Omidshafiei · David Balduzzi · Max Jaderberg -
2019 Workshop: Bridging Game Theory and Deep Learning »
Ioannis Mitliagkas · Gauthier Gidel · Niao He · Reyhane Askari Hemmat · N H · Nika Haghtalab · Simon Lacoste-Julien -
2019 Demonstration: Real Time CFD simulations with 3D Mesh Convolutional Networks »
Pierre Baque · Pascal Fua · François Fleuret -
2019 Poster: Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks »
Gauthier Gidel · Francis Bach · Simon Lacoste-Julien -
2019 Poster: Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates »
Sharan Vaswani · Aaron Mishkin · Issam Laradji · Mark Schmidt · Gauthier Gidel · Simon Lacoste-Julien -
2019 Poster: Full-Gradient Representation for Neural Network Visualization »
Suraj Srinivas · François Fleuret -
2019 Poster: Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics »
Giancarlo Kerg · Kyle Goyette · Maximilian Puelma Touzel · Gauthier Gidel · Eugene Vorontsov · Yoshua Bengio · Guillaume Lajoie -
2018 Workshop: Smooth Games Optimization and Machine Learning »
Simon Lacoste-Julien · Ioannis Mitliagkas · Gauthier Gidel · Vasilis Syrgkanis · Eva Tardos · Leon Bottou · Sebastian Nowozin -
2018 Poster: Quantifying Learning Guarantees for Convex but Inconsistent Surrogates »
Kirill Struminsky · Simon Lacoste-Julien · Anton Osokin -
2018 Poster: Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching »
Stepan Tulyakov · Anton Ivanov · François Fleuret -
2017 Poster: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization »
Fabian Pedregosa · Rémi Leblond · Simon Lacoste-Julien -
2017 Poster: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2017 Spotlight: Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization »
Fabian Pedregosa · Rémi Leblond · Simon Lacoste-Julien -
2017 Oral: On Structured Prediction Theory with Calibrated Convex Surrogate Losses »
Anton Osokin · Francis Bach · Simon Lacoste-Julien -
2017 Poster: K-Medoids For K-Means Seeding »
James Newling · François Fleuret -
2017 Spotlight: K-Medoids For K-Means Seeding »
James Newling · François Fleuret -
2016 Poster: Nested Mini-Batch K-Means »
James Newling · François Fleuret -
2016 Poster: PAC-Bayesian Theory Meets Bayesian Inference »
Pascal Germain · Francis Bach · Alexandre Lacoste · Simon Lacoste-Julien -
2015 Poster: On the Global Linear Convergence of Frank-Wolfe Optimization Variants »
Simon Lacoste-Julien · Martin Jaggi -
2015 Poster: Barrier Frank-Wolfe for Marginal Inference »
Rahul G Krishnan · Simon Lacoste-Julien · David Sontag -
2015 Poster: Variance Reduced Stochastic Gradient Descent with Neighbors »
Thomas Hofmann · Aurelien Lucchi · Simon Lacoste-Julien · Brian McWilliams -
2015 Poster: Kullback-Leibler Proximal Variational Inference »
Mohammad Emtiyaz Khan · Pierre Baque · François Fleuret · Pascal Fua -
2015 Poster: Rethinking LDA: Moment Matching for Discrete ICA »
Anastasia Podosinnikova · Francis Bach · Simon Lacoste-Julien -
2014 Demonstration: A 3D Simulator for Evaluating Reinforcement and Imitation Learning Algorithms on Complex Tasks »
Leonidas Lefakis · François Fleuret · Cijo Jose -
2014 Poster: SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives »
Aaron Defazio · Francis Bach · Simon Lacoste-Julien -
2013 Poster: Reservoir Boosting : Between Online and Offline Ensemble Learning »
Leonidas Lefakis · François Fleuret -
2011 Poster: Boosting with Maximum Adaptive Sampling »
Charles Dubout · François Fleuret -
2010 Demonstration: Platform to Share Feature Extraction Methods »
François Fleuret -
2010 Poster: Joint Cascade Optimization Using A Product Of Boosted Classifiers »
Leonidas Lefakis · François Fleuret -
2009 Workshop: The Generative and Discriminative Learning Interface »
Simon Lacoste-Julien · Percy Liang · Guillaume Bouchard -
2008 Poster: DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification »
Simon Lacoste-Julien · Fei Sha · Michael Jordan