znxlwm/tensorflow-MNIST-cGAN-cDCGAN znxlwm/tensorflow-MNIST-cGAN-cDCGAN

  • Stars
    star
    147
  • Rank 251,347 (Top 5 %)
  • Language
    Python
  • Created over 7 years ago
  • Updated over 7 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
znxlwm/tensorflow-MNIST-cGAN-cDCGAN
github

znxlwm

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

tensorflow-MNIST-cGAN-cDCGAN

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) [1] and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MANIST [2] dataset.

Implementation details

  • cGAN

GAN

Resutls

  • Generate using fixed noise (fixed_z_)
cGAN cDCGAN
  • MNIST vs Generated images
MNIST cGAN after 100 epochs cDCGAN after 30 epochs
  • Training loss
cGAN cDCGAN
  • Learning time
    • MNIST cGAN - Avg. per epoch: 3.21 sec; Total 100 epochs: 1800.37 sec
    • MNIST cDCGAN - Avg. per epoch: 53.07 sec; Total 30 epochs: 2072.29 sec

Development Environment

  • Windows 7
  • GTX1080 ti
  • cuda 8.0
  • Python 3.5.3
  • tensorflow-gpu 1.2.1
  • numpy 1.13.1
  • matplotlib 2.0.2
  • 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.

More Repositories

1

pytorch-generative-model-collections

Collection of generative models in Pytorch version.
Python
2,588
star
2

UGATIT-pytorch

Official PyTorch implementation of U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
Python
2,508
star
3

pytorch-MNIST-CelebA-GAN-DCGAN

Pytorch implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Networks (DCGAN) for MNIST and CelebA datasets
Python
507
star
4

pytorch-MNIST-CelebA-cGAN-cDCGAN

Pytorch implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) for MNIST dataset
Python
482
star
5

pytorch-CartoonGAN

Pytorch implementation of CartoonGAN (CVPR 2018)
Python
388
star
6

tensorflow-MNIST-GAN-DCGAN

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.
Python
175
star
7

pytorch-pix2pix

Pytorch implementation of pix2pix for various datasets.
Python
119
star
8

pytorch-Conditional-image-to-image-translation

Pytorch implementation of Conditional image-to-image translation (CVPR 2018)
Python
48
star
9

pytorch-apex-experiment

Simple experiment of Apex (A PyTorch Extension)
Python
47
star
10

pytorch-CycleGAN

Pytorch implementation of CycleGAN.
Python
40
star
11

FUNIT-pytorch

Pytorch implementation of "Few-Shot Unsupervised Image-to-Image Translation" (ICCV 2019)
Python
36
star
12

tensorflow-pix2pix

Tensorflow implementation of pix2pix for various datasets.
Python
6
star