https://github.com/znxlwm/pytorch-mnist-celeba-cgan-cdcgan

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
https://github.com/znxlwm/pytorch-mnist-celeba-cgan-cdcgan

cdcgan celeba cgan conditional-dcgan conditional-gan gender generative-adversarial-network mnist pytorch

Last synced: 5 days ago
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Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset

• Host: GitHub
• URL: https://github.com/znxlwm/pytorch-mnist-celeba-cgan-cdcgan
• Owner: znxlwm
• Created: 2017-07-24T06:35:40.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-08-22T09:04:24.000Z (over 7 years ago)
• Last Synced: 2025-01-13T09:05:53.166Z (13 days ago)
• Topics: cdcgan, celeba, cgan, conditional-dcgan, conditional-gan, gender, generative-adversarial-network, mnist, pytorch
• Language: Python
• Homepage:
• Size: 45.7 MB
• Stars: 501
• Watchers: 11
• Forks: 129
• Open Issues: 11
• Metadata Files:
  • Readme: README.md

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README

        
# pytorch-MNIST-CelebA-cGAN-cDCGAN

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) [1] and conditional Generative Adversarial Networks (cDCGAN) for MNIST [2] and CelebA [3] datasets.

* The network architecture (number of layer, layer size and activation function etc.) of this code differs from the paper.

* CelebA dataset used gender lable as condition.

* If you want to train using cropped CelebA dataset, you have to change isCrop = False to isCrop = True.

* you can download

  - MNIST dataset: http://yann.lecun.com/exdb/mnist/

  - CelebA dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

## Implementation details

* cGAN

![GAN](pytorch_cGAN.png)

* cDCGAN

![Loss](pytorch_cDCGAN.png)

## Resutls

### MNIST

* Generate using fixed noise (fixed_z_)

 cGAN

 cDCGAN





* MNIST vs Generated images

 MNIST 

 cGAN after 50 epochs 

 cDCGAN after 20 epochs 







* Learning Time

  * MNIST cGAN - Avg. per epoch: 9.13 sec; Total 50 epochs: 937.06 sec

  * MNIST cDCGAN - Avg. per epoch: 47.16 sec; Total 20 epochs: 1024.26 sec

### CelebA

* Generate using fixed noise (fixed_z_; odd line - female (y: 0) & even line - male (y: 1); each two lines have the same style (1-2) & (3-4).)

 cDCGAN

 cDCGAN crop





* CelebA vs Generated images

 CelebA 

 cDCGAN after 20 epochs 

 cDCGAN crop after 30 epochs 







* CelebA cDCGAN morphing (noise interpolation)

 cDCGAN 

 cDCGAN crop 





* Learning Time

  * CelebA cDCGAN - Avg. per epoch: 826.69 sec; total 20 epochs ptime: 16564.10 sec

## Development Environment

* Ubuntu 14.04 LTS

* NVIDIA GTX 1080 ti

* cuda 8.0

* Python 2.7.6

* pytorch 0.1.12

* torchvision 0.1.8

* matplotlib 1.3.1

* imageio 2.2.0

## Reference

[1] Mirza, Mehdi, and Simon Osindero. "Conditional generative adversarial nets." arXiv preprint arXiv:1411.1784 (2014).

(Full paper: https://arxiv.org/pdf/1411.1784.pdf)

[2] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE, 86(11):2278-2324, November 1998.

[3] Liu, Ziwei, et al. "Deep learning face attributes in the wild." Proceedings of the IEEE International Conference on Computer Vision. 2015.