zhykoties / improved_ccgan

Continuous Conditional Generative Adversarial Networks for Image Generation: Novel Losses and Label Input Mechanisms

Continuous Conditional GAN (CcGAN)

If you use this code, please cite

@inproceedings{
      ding2021ccgan,
      title={Cc{\{}GAN{\}}: Continuous Conditional Generative Adversarial Networks for Image Generation},
      author={Xin Ding and Yongwei Wang and Zuheng Xu and William J Welch and Z. Jane Wang},
      booktitle={International Conference on Learning Representations},
      year={2021},
      url={https://openreview.net/forum?id=PrzjugOsDeE}
}

@misc{ding2020continuous,
      title={Continuous Conditional Generative Adversarial Networks for Image Generation: Novel Losses and Label Input Mechanisms}, 
      author={Xin Ding and Yongwei Wang and Zuheng Xu and William J. Welch and Z. Jane Wang},
      year={2020},
      eprint={2011.07466},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

1. Datasets

The RC-49 Dataset (h5 file)

https://1drv.ms/u/s!Arj2pETbYnWQr7MY2Pr5qipSUpZKEQ?e=aRym3k

Download 'RC-49_64x64.h5' and put it in './improved_CcGAN/dataset/RC-49'

The preprocessed UTKFace Dataset (h5 file)

https://1drv.ms/u/s!Arj2pETbYnWQr7MW_sGY9tJC4G3eMw?e=ohhRTe

Download 'UTKFace_64x64.h5' and put it in './improved_CcGAN/dataset/UTKFace'

The Cell-200 dataset (h5 file)

https://1drv.ms/u/s!Arj2pETbYnWQr8tDP9Etf16nWddoTQ

Download 'Cell200_64x64.h5' and put it in './improved_CcGAN/dataset/Cell200'

The Steering Angle dataset (h5 file)

For CcGAN, AE, and Regression CNN training:
https://1drv.ms/u/s!Arj2pETbYnWQr7Mdwe6H-IS0YwXh3A?e=U0BiIq

For Clssification CNN training:
https://1drv.ms/u/s!Arj2pETbYnWQr8xEgY3ZHSe2b1CHlQ?e=SE7pv6

Download 'SteeringAngle_64x64.h5' and 'SteeringAngle_5_scenes_64x64' and put them in './improved_CcGAN/dataset/SteeringAngle'

2. Sample Usage

If a folder has 'improved' in its name, this folder corresponds to a ILI-based CcGAN; otherwise, a NLI-based CcGAN.

2.1 Simulation ('./improved_CcGAN/Simulation')

First, set the ROOT_PATH and DATA_PATH in the './scripts/run_train.sh' to yours.

Then, run 'run_train.sh'.

2.2 RC-49 ('./improved_CcGAN/RC-49' and './improved_CcGAN/RC-49-improved'')

First, set the ROOT_PATH and DATA_PATH in the './scripts/run_train.sh' to yours.

Then, run 'run_train.sh'.

2.3 UTKFace ('./improved_CcGAN/UTKFace' and './improved_CcGAN/UTKFace-improved')

First, set the ROOT_PATH and DATA_PATH in './scripts/run_train.sh' to yours.

Then, run 'run_train.sh'.

2.4 Cell-200 ('./improved_CcGAN/Cell200' and './improved_CcGAN/Cell200-improved')

First, set the ROOT_PATH and DATA_PATH in './scripts/run_train.sh' to yours.

Then, run 'run_train.sh'.

2.5 Steering Angle ('./improved_CcGAN/SteeringAngle' and './improved_CcGAN/SteeringAngle-improved')

First, set the ROOT_PATH and DATA_PATH in './scripts/run_train.sh' to yours.

Then, run 'run_train.sh'.

3. NIQE

The code for computing NIQE is in './improved_CcGAN/NIQE'. Corresponding tutorial will be provided soon.