DGMHowTo

A collection of vanilla Deep Generative Models (DGM) re-implementation with clean and well-annotated PyTorch implementation for systematic learning toward Deep Generative Models.

Support Models

• Generative Adversarial Networks (GAN)
• Variational Auto-Encoder (VAE)
• Neural Autoregressive Distribution Estimation (NADE)

Get Started

# install dependencies
pip install -r requirements.txt

# prepare your dataset in `data`

# prepare your experiment configuration in `configs`

# train the model
python train_*.py --config configs/*.txt

Results

GAN

• loss process graph

• noise generation process

• noise discriminator accuracy process

VAE

• loss process graph

• noise generation process

NADE

• loss process graph

• random generation process

Note

Kudos to the authors for their amazing results.

@article{Kingma2014AutoEncodingVB,
  title={Auto-Encoding Variational Bayes},
  author={Diederik P. Kingma and Max Welling},
  journal={CoRR},
  year={2014},
  volume={abs/1312.6114}
}

@article{goodfellow2020generative,
  title={Generative adversarial networks},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Communications of the ACM},
  volume={63},
  number={11},
  pages={139--144},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@inproceedings{larochelle2011neural,
  title={The neural autoregressive distribution estimator},
  author={Larochelle, Hugo and Murray, Iain},
  booktitle={Proceedings of the fourteenth international conference on artificial intelligence and statistics},
  pages={29--37},
  year={2011},
  organization={JMLR Workshop and Conference Proceedings}
}