WGANs: A stable alternative to traditional GANs Wasserstein GAN - видео

In this video well explore the Wasserstein GAN with Gradient Penalty which addresses the instability issues in traditional GANs. Unlike traditional GANs WGANs use the Wasserstein distance as their loss function to measure the difference between the real and generated data distributions. The Gradient penalty is used to ensure that the gradients from the discriminator dont explode or vanish. Well implement the WGAN with Gradient Penalty from scratch and use the anime faces dataset for training. Watc...
Источник видео: RuTube.ru (Обучение)