caojiezhang/LCCGAN-v2

Code for “Improving Generative Adversarial Networks with Local Coordinate Coding”

LCCGAN-v2

• LCCGAN-v1: Pytorch implementation

Pytorch implementation for “Improving Generative Adversarial Networks with Local Coordinate Coding”.

• AutoEncoder (AE) learns the embeddings on the latent manifold.

• Local Coordinate Coding (LCC) learns local coordinate systems. Specifically, we train LCCGAN-v1 with q=2 and LCCGAN-v2 with q=3.

• The LCC sampling method is conducted on the latent manifold.

• The LCCGAN is a general framework that can be applied to different GAN methods.

Dependencies

python 2.7

Pytorch 0.4

Dataset

In our paper, to sample different images, we train our model on four datasets, respectively.

• Download MNIST dataset.

• Download Oxford-102 Flowers dataset.

• Download Large-scale CelebFaces Attributes (CelebA) dataset.

• Download Large-scale Scene Understanding (LSUN) dataset.

Training

• Train LCCGAN-v2 on MNIST dataset.

  • python trainer.py --dataset mnist --dataroot ./mnist --nc 1

• Train LCCGAN-v2 on Oxford-102 Flowers dataset.

  • python trainer.py --dataset Oxford-102 --dataroot your_images_folder

• If you want to train the model on Large-scale CelebFaces Attributes (CelebA), Large-scale Scene Understanding (LSUN) or your own dataset. Just replace the hyperparameter like these:

  • python trainer.py --dataset name_o_dataset --dataroot path_of_dataset

Citation

@InProceedings{pmlr-v80-cao18a,
  title = 	 {Adversarial Learning with Local Coordinate Coding},
  author = 	 {Cao, Jiezhang and Guo, Yong and Wu, Qingyao and Shen, Chunhua and Huang, Junzhou and Tan, Mingkui},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {707--715},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Stockholmsmässan, Stockholm Sweden},
  month = 	 {10--15 Jul},
  publisher = 	 {PMLR}
}