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Lr-LiVAE (CVPR2019)

Tensorflow implementation of Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions.

The network architecture


Exemplar Results

  • Toy experiment


  • Generated images


  • Face synthesis


Preparation

  • Prerequisites
    • Tensorflow
    • Python 2.x with matplotlib, numpy and scipy
  • Dataset
    • FaceScrub
      • Images should be placed in ./Datas/facescrub_aligned/
  • pre-trained model
    • Download the trained model from Baidu Cloud(dfna), and unzip the files to ./models/

Quick Start

Exemplar commands are listed here for a quick start.

Training

  • To train on facescrub dataset with size of 64 X 64

    python Lr-LiVAE.py --mode training

Testing

  • Example of generating random samples

    python Lr-LiVAE.py --mode generation

  • Example of image inpainting

    python Lr-LiVAE.py --mode inpainting

  • Example of face synthesis using z_s and z_u from different images

    python Lr-LiVAE-GAN.py --mode exchanging

Citation

If this work is useful for your research, please consider citing:

@inproceedings{zheng2019disentangling,
  title={Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions},
  author={Zheng, Zhilin and Sun, Li},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={12192--12201},
  year={2019}
}