TL;DR
This paper explains the mathematical foundations of GANs and introduces Wasserstein GANs to improve training stability by using a smoother metric for distribution comparison.
Contribution
It provides a detailed explanation of GAN mathematics and proposes Wasserstein GANs as a novel approach to enhance training stability.
Findings
Wasserstein GANs improve training stability over traditional GANs.
The paper clarifies the mathematical challenges in training GANs.
Wasserstein distance offers a smoother metric for distribution comparison.
Abstract
This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs' training by adopting a smooth metric for measuring the distance between two probability distributions.
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Gaussian Processes and Bayesian Inference · Stochastic Gradient Optimization Techniques
MethodsConvolution · Dogecoin Customer Service Number +1-833-534-1729