Implementation of GAN models in PyTorch.
Project description
GAN-zoo
Train and evaluate basic FC-GAN:
# train on fashion-mnist
python -m ganzoo.examples.basic_gan.train_fc \
--z_dim=100 --num_hidden_units=256 \
--network_type=fc-large \
--dataset_name=fashion-mnist
# evaluate
python -m ganzoo.examples.basic_gan.eval_fc \
lightning_logs/version_1/checkpoints/epoch\=99-step\=337600.ckpt \
--output_dir lightning_logs/version_1/outputs
A collection of GAN models implemented in PyTorch:
References - part 1
- GAN arxiv
- Wasserstein GAN arxiv
- DCGAN arxiv
- Conditional GANs arxiv
- LSGAN arxiv
- SAGAN arxiv
- Pix2Pix arxiv
- Pix2Pix-HD arxiv PyTorch imp.
- CycleGAN arxiv code
- StarGAN arxiv PyTorch imp.
- Geometric GAN arxiv
- InfoGAN arxiv
- BigGAN arxiv
- ConSinGAN arxiv code
- OT-GAN arxiv
References - part 2
- Improved Techniques for Training GANs arxiv
- On Convergence and Stability of GANs arxiv
- Which Training Methods for GANs do actually Converge? arxiv
- Is generator conditioning causally related to GAN performance? arxiv
- The unusual effectiveness of averaging in gan training arxiv
- cGANs with projection discriminator arxiv
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pytorch-gan-zoo-0.0.5.tar.gz
(14.6 kB
view hashes)
Built Distribution
Close
Hashes for pytorch_gan_zoo-0.0.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5342e2d8aa07f4f69e48c0cd7d09e1aabcb64dd8045bd17f4325adfcb817d0d2 |
|
MD5 | cbbd9bc5192049ad6815021a504c325b |
|
BLAKE2b-256 | 18b87946efac4a922d282d78e51d16716c0e22d07d7156078865b39ded472aed |