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

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

• cDCGAN

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.