Project referenced from Udacity's face_generation wich is part of their deep-learning course. Further implementations have been done for DCGAN and Improved WGAN with techniques to improve training speed.

• Jupyter notebook
• Tensoflow
• Matplotlib
• Numpy

1. git clone + repo URL
2. cd to repo
3. pip install -r requirement.txt if packages are not yet installed
4. jupyter notebook + jupyter notebook selected

1. Generate images with DCGAN like architecture
2. Implemented improved WGAN

• Tensoflow - Software library for numerical computation using data flow graphs
• Jupyter notebook - Web-based notebook environment for interactive computing
• Matplotlib - Python 2D plotting library
• Numpy - package for scientific computing

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

• Udacity - Initial work/Notebook boiler plate - Repo
• Jorge Ceja - Model implementations - Account

• Improved Techniques for Training GANs - arXiv
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks - arXiv
• Improved Training of Wasserstein GANs - arXiv