Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking

Traditional generative adversarial networks (GAN) and many of its variants are trained by minimizing the KL or JS-divergence loss that measures how close the generated data distribution is from the true data distribution. A recent advance called the WGAN based on Wasserstein distance can improve on the KL and JS-divergence based GANs, and alleviate the gradient vanishing, instability, and mode collapse issues that are common in the GAN training... (read more)

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METHOD TYPE
Convolution
Convolutions
WGAN
Generative Adversarial Networks
GAN
Generative Models