Variational Autoencoder
Modified to enable additional features:
- Includes a subsample of celebA dataset.
- Includes an extracted vector, defining the feature of having sunglasses
- interpolate.py : Interpolates two images
- extract_feature.py : Extracts mean vector for certain features (e.g having sunglasses). To run this, you need the entire celebA dataset.
- add_feature.py : Adds/removes feature to photos. Currently only usable feature is sunglasses.
Note: If you are looking to create sharp and realistic images, then using adversarial training can be helpful. State-of-the-art models are CycleGAN and DiscoGAN
This is a simple variational autoencoder written in Pytorch and trained using the CelebA dataset.
The images are scaled down to 112x128, the VAE has a latent space with 200 dimensions and it was trained for nearly 90 epochs.
Results
Face transitions
Mean face between two samples
