Deep Feature Consistent Variational Autoencoder in PyTorch

A PyTorch implementation of Deep Feature Consistent Variational Autoencoder. I implemented DFC-VAE based on the paper by Xianxu Hou, Linlin Shen, Ke Sun, Guoping Qiu. I trained this model with CelebA dataset. For more details about the dataset, please refer to the website.

Installation

• Clone this repository.
• python 3.6 is recommended.
• Use command pip install -r requirements.txt to install libraries.

Dataset

• You need to download the CelebA dataset from the website and arrange them like below.

.
├── README.md
├── requirements.txt
├── models
├── utils
...
└── data
    └── celeba
        ├── images
        │   ├── 000001.jpg
        │   └── ...
        └── annotations
            ├── list_attr_celeba.txt
            └── ...

Train

• Use command below.

sh run.sh

Interpolate

• Use command below.

sh interpolate.sh

ToDo

• Add experiments with other datasets(with more large image size).