most citedInstability and Local Minima in GAN Training with Kernel Discriminators

5 citations · 6 across the 3 of their papers we have counts for

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cs.LG2023★ 1 cited

Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks

Evan Becker, Parthe Pandit, Sundeep Rangan +1

Generative Adversarial Networks (GANs) are a popular formulation to train generative models for complex high dimensional data. The standard method for training GANs involves a grad…

cs.LG2022★ 5 cited

Instability and Local Minima in GAN Training with Kernel Discriminators

Evan Becker, Parthe Pandit, Sundeep Rangan +1

Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data. Despite their empirical success, the training of GANs is not fully understood…