Wasserstein GANs: How Earth Mover's Distance, Kantorovich-Rubinstein Duality, and Gradient Penalties Fixed Generative Adversarial Training
Wasserstein GANs: How Earth Mover's Distance, Kantorovich-Rubinstein Duality, and Gradient Penalties Fixed Generative Adversarial Training Generative Adversarial Networks (Goodfellow et al., 2014) established an influential paradigm for generative modeling: formulating data synthesis as a minimax game between a generator mapping latent noise to data space and a discriminator distinguishing synthetic samples from real empirical data. Despite early empirical success, the original GAN formulation
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