Gradient descent on MMD GAN parameter space converges globally to target distribution.
problem Convergence of gradient descent in Maximum Mean Discrepancy (MMD) GANs.
method Proposes a parametric kernelized gradient flow that mimics the min-max game in gradient regularized MMD GAN.
result Gradient descent on the generator's parameter space in gradient regularized MMD GAN is globally convergent to the target distribution under certain conditions.
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimizat…
Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performanc…
A new method for learning kernel spectral distributions using deep neural networks.
problem The challenge of kernel selection in machine learning.
method Implicit Kernel Learning (IKL) using deep neural networks to learn kernel spectral distributions.
result IKL improves kernel performance in both generative adversarial networks and supervised learning.
Improves GAN training with a repulsive loss function.
problem Discourages learning of fine details in data.
method Proposes a repulsive loss function and a bounded Gaussian kernel.
result Significantly improves GAN performance without additional computational cost.
KernelNet learns data-dependent kernels for deep generative models.
problem Learning kernels requires careful selection of hyperparameters.
method KernelNet constructs a data-dependent kernel using deep neural networks.
result KernelNet achieves better performance in deep generative models.
Estimates exponential family distributions using a novel doubly dual embedding technique.
problem Estimating exponential family distributions with smoothness and efficiency.
method Doubly dual embedding for avoiding partition function computation and flexible sampling.
result Improves memory and time efficiency while offering stronger statistical properties.
We propose a principled method for gradient-based regularization of the critic of GAN-like models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy (MMD). We show that controlling the gradient of the critic is vital to having a sensible loss function, and devise a method to enforce exact, ana…
Study on GANs learning distributions, deriving rates and regularization.
problem Learning distributions with GANs.
method Analysis of GANs through regularization theory.
result Optimal rates for distribution estimation under adversarial framework.
Survey of GANs and autoencoders, addressing mode collapse and likelihood issues.
problem Addressing mode collapse and likelihood issues in GANs and autoencoders.
method Explains various GAN and autoencoder variants, their applications, and methods to resolve issues.
result Various methods to resolve mode collapse and improve likelihood in GANs and autoencoders.
A new method uses characteristic functions to improve GANs.
problem Improving the stability and efficiency of Generative Adversarial Networks (GANs).
method Formulating IGM learning as minimizing characteristic function distance, using linear-time CFD computation.
result The proposed method outperforms existing GAN variants on image generation benchmarks.
A new method optimizes kernels for GANs and SVMs using mean-field theory.
problem Optimizing kernels for GANs and SVMs in a distributionally robust setting.
method Distributionally robust optimization, Monte-Carlo SAA, particle SGD, mean-field analysis.
result The method improves kernel learning for hypothesis testing and achieves better test power.
New method improves GANs by estimating density ratios in feature space with SP loss.
problem Filtering out unrealistic images from GANs trained with suboptimal discriminators.
method Develops DRE-F-SP method based on Softplus loss for density ratio estimation in feature space, and proposes three subsampling methods.
result Empirically shows substantial improvement over existing methods on synthetic and CIFAR-10 datasets.