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DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images

By Taihong Xiao, Jiapeng Hong and Jinwen Ma

If you find it useful, please cite our paper.

@article{xiao2018dna,
  title={DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images},
  author={Xiao, Taihong and Hong, Jiapeng and Ma, Jinwen},
  journal={International Conference on Learning Representations, Workshop},
  year={2018}
}

Note the code in this repo is not optimized for speed. A more efficient Pytorch version for higher resolution images will be released soon. Please stay tuned!  I have developed a new method ELEGANT for better performance and efficiency, please visit here.

Introduction

This repo is the official TensorFlow implementation for our paper DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images (OpenReview, ArXiv). DNA-GAN is a supervised method for disentangling multiple factors of variation simultaneously by using multi-attribute images. It can manipulate several attributes in the latent representations of images, which is a generalization of GeneGAN. However, DNA-GAN replaces the explicit nulling loss with the annihilating operation and employes a single discriminator for guiding images generation on multiple attributes.

A and B are two images with different label at i-th position. Through an encoder, we can obtain two DNA pieces (latent representations). By the annihilating the recessive pattern and swapping two pieces, we could obtain another two DNA pieces. Therefore, we could obtain four images through an decoder. The reconstruction loss is used to ensure the consistency between A1 and A, B1 and B. The adversarial discriminator loss helps generate novel images A2 and B2. Through iterative training, DNA-GAN is able to disentangle all factors of variations.

Requirement

• Python 2.7 or 3.5
• TensorFlow 1.3 or higher
• OpenCV 3.2

Training DNA-GAN on CelebA Dataset

0. Download celebA dataset and unzip it into datasets directory. There are various source providers for CelebA datasets. To ensure that the size of downloaded images is correct, please run identify datasets/celebA/data/000001.jpg. The size should be 409 x 687 if you are using the same dataset. Besides, please ensure that you have the following directory tree structure in your repo.

├── datasets
│   └── celebA
│       ├── data
│       ├── list_attr_celeba.txt
│       └── list_landmarks_celeba.txt

1. Run python preprocess.py. It will take only few minutes to preprocess all images. A new directory datasets/celebA/align_5p will be created.

2. Run python create_tfrecords.py to obtain tfrecords files.

3. Run python train.py -a Bangs Eyeglasses -g 0 to train DNA-GAN with respect to two attributes Bangs and Eyeglasses simultaneuously. You can play with other attributes as well. Please refer to list_attr_celeba.txt for all available attributes.

4. Run tensorboard --logdir=./train_log/logs --port=6006 to watch your training process.

Manipulating Multiple Attributes

We provided three kinds of mode for your manipulating. Run python test.py -h for detailed help.

1. Interpolation on One Attribute

You can also interpolating the process of adding eyeglasses to one person by running

python test.py -m interpolation -a Bangs Eyeglasses --swap_list 1 -i input_img.jpg -t target_img.jpg -n 5

--swap_list gives the indices of attributes to do interpolation. -n 5 gives the interpolation length.

2. Matrix Interpolation on One Attributes

What if I want to change my hairstyle everyday? No problem. Take your selfie as input, and several hair models as targets images.

python test.py -m matrix -a Bangs Eyeglasses --swap_list 0 -i input_img.jpg --targets target_img0.jpg -target_img1.jpg target_img2.jpg target_img3.jpg -s 5 5

Which hairstyle suits you best?

3. Interpolation on Two Attributes

The most amazing thing is that we can interpolating two attributes in the same time.

python test.py -m interpolation2 -a Bangs Eyeglasses --swap_list 0 1 -i input_img.jpg --targets target_img0.jpg target_img1.jpg -s 5 5

For more interpolation results, please refer to our paper.