TY - GEN
T1 - SGD Learns One-Layer networks in WGANs
AU - Lei, Qi
AU - Lee, Jason D.
AU - Dimakis, Alexandros G.
AU - Daskalakis, Constantinos
N1 - Publisher Copyright:
© International Conference on Machine Learning, ICML 2020. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax optimization problem to global optimality, but are in practice successfully trained using stochastic gradient descent-ascent. In this paper, we show that, when the generator is a one_layer network, stochastic gradient descent-ascent converges to a global solution with polynomial time and sample complexity.
AB - Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of the most successful variants of GANs, require solving a minmax optimization problem to global optimality, but are in practice successfully trained using stochastic gradient descent-ascent. In this paper, we show that, when the generator is a one_layer network, stochastic gradient descent-ascent converges to a global solution with polynomial time and sample complexity.
UR - http://www.scopus.com/inward/record.url?scp=85105600670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85105600670&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85105600670
T3 - 37th International Conference on Machine Learning, ICML 2020
SP - 5755
EP - 5764
BT - 37th International Conference on Machine Learning, ICML 2020
A2 - Daume, Hal
A2 - Singh, Aarti
PB - International Machine Learning Society (IMLS)
T2 - 37th International Conference on Machine Learning, ICML 2020
Y2 - 13 July 2020 through 18 July 2020
ER -