/pytorch-MNIST-CelebA-GAN-DCGAN

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets

Primary LanguagePython

pytorch-MNIST-CelebA-GAN-DCGAN

Pytorch implementation of Generative Adversarial Networks (GAN) [1] and Deep Convolutional Generative Adversarial Networks (DCGAN) [2] for MNIST [3] and CelebA [4] datasets.

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

  • you can download

  • pytorch_CelebA_DCGAN.py requires 64 x 64 size image, so you have to resize CelebA dataset (celebA_data_preprocess.py).

  • pytorch_CelebA_DCGAN.py added learning rate decay code.

Implementation details

  • GAN

GAN

  • DCGAN

Loss

Resutls

MNIST

  • Generate using fixed noise (fixed_z_)
GAN DCGAN
  • MNIST vs Generated images
MNIST GAN after 100 epochs DCGAN after 20 epochs

  • Training loss

    • GAN Loss

  • Learning Time

    • MNIST DCGAN - Avg. per epoch: 197.86 sec; (if you want to reduce learning time, you can change 'generator(128)' and 'discriminator(128)' to 'generator(64)' and 'discriminator(64)' ... then Avg. per epoch: about 67sec in my development environment.)

CelebA

  • Generate using fixed noise (fixed_z_)
DCGAN DCGAN crop
  • CelebA vs Generated images
CelebA DCGAN after 20 epochs DCGAN crop after 30 epochs
  • Learning Time
    • CelebA DCGAN - Avg. per epoch: 732.54 sec; total 20 epochs ptime: 14744.66 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
  • scipy 0.19.1

Reference

[1] Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems. 2014.

(Full paper: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)

[2] Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434 (2015).

(Full paper: https://arxiv.org/pdf/1511.06434.pdf)

[3] 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.

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