AnimeGAN
DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2
| AnimeGAN | DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2 | |
|---|---|---|
| 1 | 2 | |
| 24 | 399 | |
| - | - | |
| 0.0 | 1.5 | |
| over 2 years ago | over 2 years ago | |
| Python | Python | |
| Creative Commons Zero v1.0 Universal | MIT License |
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AnimeGAN
DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2
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[D]: Vanishing Gradients and Resnets
I am assuming you are doing a WGAN approach since that would explain the gradient penalty violation. In this case, use LayerNorm as indicated here: https://github.com/LynnHo/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2/issues/3
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[D] Create Labels for Data created by a GAN
Thanks so much for the fast reply! I'm still fairly new and your help is highly appreciated! Let's have a look at the WGAN from here. How do I make that conditional? Do I just add a Layer at the beginning of module.ConvGenerator and module.ConvDiscriminator? Do I need to update anything else? Like the loss i.e. or the rest of the Layer? Does the resolution of the following layers change?
What are some alternatives?
EigenGAN-Tensorflow - EigenGAN: Layer-Wise Eigen-Learning for GANs (ICCV 2021)
wgan-gp - A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Anime-Generation - 🎨 Anime generation with GANs.
hifigan-denoiser - HiFi-GAN: High Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks