This work was introduced in the summer research program supervised by Prof. Vijayakumar Bhagavatula hosted by Carnegie Mellon University after I finish my second year study in computer science department.
Currently, generative adversarial net is widely used in domain adaptation, image to image translation, adversarial training and etc. A bunch of different GANs are proposed to solve these problems, most of them proposed a new loss function and experiment on image datasets. But nearly none of them explain GAN back to the probability view. In this project, I explore the insight of GAN, simGAN and cycleGAN in distribution level.
Pytorch with cpu Version
mixGau-simGAN.py generated mixture Gaussian data from mixture Gaussian Distribution using simGAN [1]
mixGau_GAN.py generated mixture Gaussian data from mixture Gaussian Distribution using GAN [2]
mixGau_cycleGAN.py generated mixture Gaussian data from mixture Gaussian Distribution using cycleGAN [3]
uniMix_GAN.py generated mixture Gaussian data from uniform Distribution using GAN
uniNor_cycleGAN.py generated mixture Gaussian data from uniform Distribution using GAN
python uniMix_GAN.py
GAN mixGaussian 2 mixGaussian https://youtu.be/2pEcTiFSMLw
GAN uniform 2 Gaussian https://youtu.be/Sq20c6R_XFw
cycleGAN mixGaussian 2 mixGaussian https://youtu.be/wvZUGTKfoLc
Example output of a single frame
- Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
- Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2016). Learning from simulated and unsupervised images through adversarial training. arXiv preprint arXiv:1612.07828.
- Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593.