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All Projects → znxlwm → Pytorch Mnist Celeba Cgan Cdcgan

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

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pytorchgenerative-adversarial-networkmnistceleba

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View All Similar Projects ➔

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.

Implementation details

  • cGAN

GAN

  • cDCGAN

Loss

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.

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