Human Face Generator

GitHub Link

A deep convolutional GAN was trained from scratch on the Large scale CelebFaces dataset consisting of 200k images of faces.

A random sample vector as well as a dropout was used in both the Discriminator and the Generator to induce noise.

Various values of dropouts were tried.

Training Method

Download the images into data/celeba/ folder in the same directory.

./train.py --d1 [argument] --d2 [argument]

d1 and d2 are dropout values that are applied on the odd and even layers of the network respectively.They can be independently chosen.

Sample Generation

./sample_generator.py --d1 [argument] --d2 [argument]

Model state dictionaries for models with different dropouts values are provided for trained models in models folder

Sample Image Results