Oct 15, 2019 · In this paper, we show that, when the generator is a one-layer network, stochastic gradient descent-ascent converges to a global solution with ...

May 5, 2023 · We show that stochastic gradient descent ascent converges to a global optimum for WGAN with one-layer generator network.

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 ...

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 ...

Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein GANs (WGANs), one of.

It is shown that, when the generator is a one-layer network, stochastic gradient descent-ascent converges to a global solution with polynomial time and ...

Jul 2, 2020 · Abstract. Generative adversarial networks (GANs) are a widely used framework for learning generative models. Wasserstein. GANs (WGANs), one ...

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Posted: Dec 16, 2020

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SGD Learns One-Layer Networks in WGANs ... Generative adversarial networks (GANs) are a widely used framework for learning generative models.