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Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks
We study the local dynamics of GDA for training a GAN with a kernel-based discriminator.
High Probability Bounds for Stochastic Continuous Submodular Maximization
This implies that for a particular run of the algorithms, the solution may be much worse than the provided guarantee in expectation.
Instability and Local Minima in GAN Training with Kernel Discriminators
Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data.
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