You call your backward functions twice with both the real and fake values loss being backpropagated at different time steps. Technically an ... ... <看更多>

PyTorch implementations of Generative Adversarial Networks. ... a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance ... ... <看更多>

Loss and Training. The network uses Earth Mover's Distance instead of Jensen-Shannon Divergence to compare probability distributions. minimax. ... <看更多>

Source code for "Progressive Growing of GANs for Improved Quality, Stability, and Variation". generator pytorch discriminator generative-adversarial-network ... ... <看更多>

The function f is the critic, i.e. a neural network, and the way this loss is implemented in PyTorch in this youtbe video (cf. minutes 11:00 ... ... <看更多>

2. no log Loss (Wasserstein distance) 3. clip param norm to c (Wasserstein distance and Lipschitz continuity) 4. No momentum-based optimizer, ... ... <看更多>

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This repository contains a PyTorch implementation of the Wasserstein GAN with gradient ... These are the plots for the generator loss, discriminator loss, ... ... <看更多>

Generalized Loss-Sensitive Generative Adversarial Networks (GLS-GAN) in PyTorch with ... Pytorch implementation of Wasserstein GANs with Gradient Penalty. ... <看更多>

Pytorch implementation of the paper (q,p)-Wasserstein GANs: Comparing Ground Metrics for ... Tries to cover various loss functions defined over the years. ... <看更多>

CheungBH/PyTorch-GAN - PyTorch implementations of Generative Adversarial ... enforcing method paired with a loss derived from the Wasserstein distance for ... ... <看更多>

pytorch implementations of generative adversarial networks. ... a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance ... ... <看更多>