/began-tensorflow

👩 Boundary Equilibrium Generative Adversarial Network Implementation in TensorFlow

Primary LanguagePython

BEGAN

Prerequisites

Getting Started

  1. git clone + repo URL
  2. cd to repo
  3. pip install -r requirement.txt if packages are not yet installed
  4. Train model: python train.py downloads dataset if not present and trains the model

Results

result

Todo

  1. Add skip connections Decoder
  2. Implement latent space interpolation
  3. Test GAN improvement techniques

Built With

  • Tensoflow - Software library for numerical computation using data flow graphs
  • Matplotlib - Python 2D plotting library
  • Numpy - package for scientific computing

Contributing

  1. Fork it! Star it?
  2. Create your feature branch: git checkout -b my-new-feature
  3. Commit your changes: git commit -am 'Add some feature'
  4. Push to the branch: git push origin my-new-feature
  5. Submit a pull request :D

Authors

  • Udacity - Helper functions - Repo
  • Jorge Ceja - Model implementations - Account

Acknowledgments

  • BEGAN: Boundary Equilibrium Generative Adversarial Networks - arXiv
  • Improved Training of Wasserstein GANs - arXiv
  • Improved Techniques for Training GANs - arXiv