Wasserstein GANs are increasingly used in Computer Vision applications as they are easier to train. Previous WGAN variants mainly use the l1 transport cost to compute the Wasserstein distance between the real and synthetic data distributions. The l1 transport cost restricts the discriminator to be 1-Lipschitz. However, WGANs with l1 transport cost were recently shown to not always converge. In this paper, we propose WGAN-QC, a WGAN with quadratic
transport cost. Based on the quadratic transport cost, we propose an Optimal Transport Regularizer (OTR) to stabilize the training process of WGAN-QC. We prove that the
objective of the discriminator during each generator update computes the exact quadratic Wasserstein distance between real and synthetic data distributions. We also prove that
WGAN-QC converges to a local equilibrium point with finite discriminator updates per generator update. We show experimentally on a Dirac distribution that WGAN-QC converges, when many of the l1 cost WGANs fail to [22]. Qualitative and quantitative results on the CelebA, CelebA-HQ, LSUN and the ImageNet dog datasets show that WGAN-QC
is better than state-of-art GAN methods. WGAN-QC has much faster runtime than other WGAN variants.
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EFFICIENT DISCRETE MULTI-MARGINAL OPTIMAL TRANSPORT REGULARIZATION
Optimal transport has emerged as a powerful tool for a variety of problems in machine learning, and it is frequently used to enforce distributional constraints. In this context, existing methods often use either a Wasserstein metric, or else they apply concurrent barycenter approaches when more than two distributions are considered. In this paper, we leverage multi-marginal optimal transport (MMOT), where we take advantage of a procedure that computes a generalized earth mover’s distance as a sub-routine. We show that not only is our algorithm computationally more efficient compared to other barycentric-based distance methods, but it has the additional advantage that gradients used for backpropagation can be efficiently computed during the forward pass computation itself, which leads to substantially faster model training. We provide technical details about this new regularization term and its properties, and we present experimental demonstrations of faster runtimes when compared to standard Wasserstein-style methods. Finally, on a range of experiments designed to assess effectiveness at enforcing fairness, we demonstrate our method compares well with alternatives.
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- Award ID(s):
- 1918211
- NSF-PAR ID:
- 10467900
- Publisher / Repository:
- OpenReview.net
- Date Published:
- Format(s):
- Medium: X
- Location:
- International Conference on Learning Representations
- Sponsoring Org:
- National Science Foundation
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