Awesome
LCCGAN
Pytorch implementation for “Adversarial Learning with Local Coordinate Coding”.
<!-- ## Demonstration of Local Coordinate Coding (LCC) <img src="./images/local_g.png" width="600px" /> -->Architecture of LCCGAN
<div align=center> <img src="./images/architecture.png" width="800px" /> </div>- AutoEncoder (AE) learns embeddings on the latent manifold.
- Local Coordinate Coding (LCC) learns local coordinate systems.
- The LCC sampling method is conducted on the latent manifold.
Gometric Views of LCC Sampling
<div align=center> <img src="./images/lcc_sampling.jpg" width="600px" /> </div>- With the help of LCC, we obtain local coordinate systems for sampling on the latent manifold.
- Using the local coordinate systems, LCC-GANs always sample some meaningful points to generate new images with different attributes.
Training Algorithm
<img src="./images/algorithm.png" width="450px" />Dependencies
python 2.7
Pytorch
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 on Oxford-102 Flowers dataset.
python trainer.py --dataset flowers --dataroot your_images_folder --batchSize 64 --imageSize 64 --cuda
- 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}
}