znxlwm / pytorch-MNIST-CelebA-GAN-DCGAN

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

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

  • MNIST dataset: http://yann.lecun.com/exdb/mnist/
  • CelebA dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

• 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

• DCGAN

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

• 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.