Generative Adversarial Networks (GANs)

Vanilla GAN and DCGAN

Requirements

• Python 3.x
• Tensorflow > 0.12
• Numpy
• SciPy
• OpenCV
• lmdb (for processing LSUN dataset only)

Pre-execution instructions

Datasets to download

Download following files in the program root dirctory (*.../gan)

• LSUN : download

Results

Vanilla GAN

• Dataset class: LSUN/church outdoor

Generated sample:

Each epoch:

Loss graph:

DCGAN

• Dataset class: LSUN/church outdoor

Generated sample:

Each epoch:

Loss graph:

Reference papers

• I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative Adversarial Nets. NIPS 2014
• I. Goodfellow. NIPS 2016 Tutorial: Generative Adversarial Networks. NIPS 2016
• A. Radford, L. Metz, and S. Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. ICLR 2016