Generate new faces using Generative Adversarial Networks (GANs).
The model is trained on the CelebFaces Attributes Dataset (CelebA):
It generates new human faces that look like this:
- Create a deeper model and use it to generate larger (say 128x128) images of faces.
- Read existing literature to see if you can use padding and normalization techniques to generate higher-resolution images.
- Implement a learning rate that evolves over time as they did in this CycleGAN Github repo.
- See if you can extend this model and use a CycleGAN to learn to swap different kinds of faces. For example, learn a mapping between faces that have and do not have eye/lip makeup, as they did in this paper.