Training Wasserstein GANs without gradient penalties

• URL: http://arxiv.org/abs/2110.14150v1
• Date: Wed, 27 Oct 2021 03:46:13 GMT
• Title: Training Wasserstein GANs without gradient penalties
• Authors: Dohyun Kwon, Yeoneung Kim, Guido Mont\'ufar, Insoon Yang
• Abstract summary: We propose a stable method to train Wasserstein generative adversarial networks. We experimentally show that this algorithm can effectively enforce the Lipschitz constraint on the discriminator. Our method requires no gradient penalties and is computationally more efficient than other methods.
• Score: 4.0489350374378645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a stable method to train Wasserstein generative adversarial networks. In order to enhance stability, we consider two objective functions using the $c$-transform based on Kantorovich duality which arises in the theory of optimal transport. We experimentally show that this algorithm can effectively enforce the Lipschitz constraint on the discriminator while other standard methods fail to do so. As a consequence, our method yields an accurate estimation for the optimal discriminator and also for the Wasserstein distance between the true distribution and the generated one. Our method requires no gradient penalties nor corresponding hyperparameter tuning and is computationally more efficient than other methods. At the same time, it yields competitive generators of synthetic images based on the MNIST, F-MNIST, and CIFAR-10 datasets.

Related papers

• Efficiently Escaping Saddle Points for Non-Convex Policy Optimization [40.0986936439803]
  Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance. We propose a variance-reduced second-order method that uses second-order information in the form of Hessian vector products (HVP) and converges to an approximate second-order stationary point (SOSP) with sample complexity of $tildeO(epsilon-3)$.
  arXiv  Detail & Related papers  (2023-11-15T12:36:45Z)
• Flow-based Distributionally Robust Optimization [23.232731771848883]
  We present a framework, called $textttFlowDRO$, for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty sets. We aim to find continuous worst-case distribution (also called the Least Favorable Distribution, LFD) and sample from it. We demonstrate its usage in adversarial learning, distributionally robust hypothesis testing, and a new mechanism for data-driven distribution perturbation differential privacy.
  arXiv  Detail & Related papers  (2023-10-30T03:53:31Z)
• Sobolev Space Regularised Pre Density Models [51.558848491038916]
  We propose a new approach to non-parametric density estimation that is based on regularizing a Sobolev norm of the density. This method is statistically consistent, and makes the inductive validation model clear and consistent.
  arXiv  Detail & Related papers  (2023-07-25T18:47:53Z)
• Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization [94.19177623349947]
  Non-smooth non optimization problems emerge in machine learning and business making. Two core challenges impede the development of efficient methods with finitetime convergence guarantee. Two-phase versions of GFM and SGFM are also proposed and proven to achieve improved large-deviation results.
  arXiv  Detail & Related papers  (2022-09-12T06:53:24Z)
• Sliced Wasserstein Variational Inference [3.405431122165563]
  We propose a new variational inference method by minimizing sliced Wasserstein distance, a valid metric arising from optimal transport. Our approximation also does not require a tractable density function of variational distributions so that approximating families can be amortized by generators like neural networks.
  arXiv  Detail & Related papers  (2022-07-26T20:51:51Z)
• Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization [94.18714844247766]
  Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. We present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures.
  arXiv  Detail & Related papers  (2021-02-02T21:01:13Z)
• Byzantine-Resilient Non-Convex Stochastic Gradient Descent [61.6382287971982]
  adversary-resilient distributed optimization, in which. machines can independently compute gradients, and cooperate. Our algorithm is based on a new concentration technique, and its sample complexity. It is very practical: it improves upon the performance of all prior methods when no. setting machines are present.
  arXiv  Detail & Related papers  (2020-12-28T17:19:32Z)
• Continuous Regularized Wasserstein Barycenters [51.620781112674024]
  We introduce a new dual formulation for the regularized Wasserstein barycenter problem. We establish strong duality and use the corresponding primal-dual relationship to parametrize the barycenter implicitly using the dual potentials of regularized transport problems.
  arXiv  Detail & Related papers  (2020-08-28T08:28:06Z)
• Projection Robust Wasserstein Distance and Riemannian Optimization [107.93250306339694]
  We show that projection robustly solidstein (PRW) is a robust variant of Wasserstein projection (WPP) This paper provides a first step into the computation of the PRW distance and provides the links between their theory and experiments on and real data.
  arXiv  Detail & Related papers  (2020-06-12T20:40:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.