(beta-)VAE

Tensorflow implementation of VAE and beta-VAE

Exemplar results

• Celeba

  • ConvNet (z_dim: 100, beta: 0.05)

    Generation	Reconstruction

• Mnist

  • ConvNet (z_dim: 10, beta: 0.1)

    Generation	Reconstruction

  • MLP (z_dim: 10, beta: 0.1)

    Generation	Reconstruction

Usage

• Prerequisites

  • Tensorflow 1.8
  • Python 2.7 or 3.6

• Examples of training

  CUDA_VISIBLE_DEVICES=0 python train.py --z_dim 10 --beta 0.1 --dataset mnist --model mlp_mnist --experiment_name z10_beta0.1_mnist_mlp
  CUDA_VISIBLE_DEVICES=0 python train.py --z_dim 10 --beta 0.1 --dataset mnist --model conv_mnist --experiment_name z10_beta0.1_mnist_conv
  CUDA_VISIBLE_DEVICES=0 python train.py --z_dim 32 --beta 0.1 --dataset celeba --model conv_64 --experiment_name z32_beta0.1_celeba_conv
  

Datasets

1. Celeba should be prepared by yourself in ./data/celeba/img_align_celeba/.jpg*
   • Download the dataset: https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AAB06FXaQRUNtjW9ntaoPGvCa?dl=0
   • the above links might be inaccessible, the alternatives are
     • img_align_celeba.zip
       • https://pan.baidu.com/s/1eSNpdRG#list/path=%2FCelebA%2FImg or
       • https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg
2. Mnist will be automatically downloaded