Awesome
Dual Contradistinctive Generative AutoEncoder (CVPR 2021)
Project | Paper | Usage | Citation
<p align="center"> <img src="https://gauravparmar.com/projects/dcvae/resources/dcvae_results.png" width="800" /> </p>A generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for there construction/synthesis), both being contradistinctive.
Usage
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Clone the repository
git clone https://github.com/mlpc-ucsd/DC-VAE cd DC-VAE
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Setup the conda environment
conda env create -f dcvae_env.yml conda activate dcvae_env
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Train the network on CIFAR-10
python train.py
Citation
If you find this project useful for your research, please cite the following work.
@InProceedings{Parmar_2021_CVPR,
author = {Parmar, Gaurav and Li, Dacheng and Lee, Kwonjoon and Tu, Zhuowen},
title = {Dual Contradistinctive Generative Autoencoder},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {823-832}
}
Credits
We found the following libraries helpful in our research.
- FID - computing the FID score
- IS - computing the Inception Score.
- AutoGAN - model architecture for the low resolution experiments experiments
- ProGAN - model architecture for the high resolution experiments.
Acknowledgements
This work is funded by NSF IIS- 1717431 and NSF IIS-1618477. We thank Qualcomm Inc. for an award support. The work was performed when G. Parmar and D. Li were with UC San Diego.