Anime-Generation
DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2
| Anime-Generation | DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2 | |
|---|---|---|
| 1 | 2 | |
| 67 | 399 | |
| - | - | |
| 0.0 | 1.5 | |
| almost 2 years ago | over 2 years ago | |
| Python | Python | |
| - | MIT License |
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Anime-Generation
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"CoPE: Conditional image generation using Polynomial Expansions", Grigorios et al 2021
anime face / getchu dataset(https://github.com/bchao1/Anime-Generation) experiment
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?
the-gan-zoo - A list of all named GANs!
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
pytorch-pretrained-BigGAN - 🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
wgan-gp - A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
Anime2Sketch - A sketch extractor for anime/illustration.
data-efficient-gans - [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
ALAE - [CVPR2020] Adversarial Latent Autoencoders
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
HyperGAN - Composable GAN framework with api and user interface
pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs