您当前的位置:首页 > IT编程 > gnn
| C语言 | Java | VB | VC | python | Android | TensorFlow | C++ | oracle | 学术与代码 | cnn卷积神经网络 | gnn | 图像修复 | Keras | 数据集 | Neo4j | 自然语言处理 | 深度学习 | 医学CAD | 医学影像 | 超参数 | pointnet | pytorch | 异常检测 | Transformers | 情感分类 | 知识图谱 |

自学教程:Keras implementations of Generative Adversarial Networks. Gan论文和代码实现

51自学网 2020-05-26 13:22:26
  gnn
这篇教程Keras implementations of Generative Adversarial Networks. Gan论文和代码实现写得很实用,希望能帮到您。
Keras implementations of Generative Adversarial Networks.
 

Files

Type Name Latest commit message Commit time
  aae Clean up in training loop 2 years ago
  acgan Update acgan.py 10 months ago
  assets WGAN (GP): Resolves #37. + clean up of handling input shapes of laten… 2 years ago
  bgan Update bgan.py 2 years ago
  bigan Clean up in training loop 2 years ago
  ccgan updated instance normalization import 15 months ago
  cgan Update cgan.py 2 years ago
  cogan Update cogan.py 2 years ago
  context_encoder Update context_encoder.py 2 years ago
  cyclegan updated instance normalization import 15 months ago
  dcgan Fix input shape of generator 2 years ago
  discogan updated instance normalization import 15 months ago
  dualgan Clean up in training loop 2 years ago
  gan removed hard-coded instances of self.latent_dim = 100 2 years ago
  infogan Clean up in training loop 2 years ago
  lsgan Update lsgan.py 2 years ago
  pix2pix updated instance normalization import 15 months ago
  pixelda updated instance normalization import 15 months ago
  sgan same fix for sgan 16 months ago
  srgan updated instance normalization import 15 months ago
  wgan Fix image rescaling to [0,1] 16 months ago
  wgan_gp change input dim in critic to use latent_dim variable 10 months ago
  .gitignore Logo 2 years ago
  LICENSE Initial commit 3 years ago

README.md

Keras-GAN

Collection of Keras implementations of Generative Adversarial Networks (GANs) suggested in research papers. These models are in some cases simplified versions of the ones ultimately described in the papers, but I have chosen to focus on getting the core ideas covered instead of getting every layer configuration right. Contributions and suggestions of GAN varieties to implement are very welcomed.

See also: PyTorch-GAN

Table of Contents

Installation

$ git clone https://github.com/eriklindernoren/Keras-GAN
$ cd Keras-GAN/
$ sudo pip3 install -r requirements.txt

Implementations

AC-GAN

Implementation of Auxiliary Classifier Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1610.09585

Example

$ cd acgan/
$ python3 acgan.py

Adversarial Autoencoder

Implementation of Adversarial Autoencoder.

Code

Paper: https://arxiv.org/abs/1511.05644

Example

$ cd aae/
$ python3 aae.py

BiGAN

Implementation of Bidirectional Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1605.09782

Example

$ cd bigan/
$ python3 bigan.py

BGAN

Implementation of Boundary-Seeking Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1702.08431

Example

$ cd bgan/
$ python3 bgan.py

CC-GAN

Implementation of Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1611.06430

Example

$ cd ccgan/
$ python3 ccgan.py

CGAN

Implementation of Conditional Generative Adversarial Nets.

Code

Paper:https://arxiv.org/abs/1411.1784

Example

$ cd cgan/
$ python3 cgan.py

Context Encoder

Implementation of Context Encoders: Feature Learning by Inpainting.

Code

Paper: https://arxiv.org/abs/1604.07379

Example

$ cd context_encoder/
$ python3 context_encoder.py

CoGAN

Implementation of Coupled generative adversarial networks.

Code

Paper: https://arxiv.org/abs/1606.07536

Example

$ cd cogan/
$ python3 cogan.py

CycleGAN

Implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1703.10593

Example

$ cd cyclegan/
$ bash download_dataset.sh apple2orange
$ python3 cyclegan.py

DCGAN

Implementation of Deep Convolutional Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1511.06434

Example

$ cd dcgan/
$ python3 dcgan.py

DiscoGAN

Implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1703.05192

Example

$ cd discogan/
$ bash download_dataset.sh edges2shoes
$ python3 discogan.py

DualGAN

Implementation of DualGAN: Unsupervised Dual Learning for Image-to-Image Translation.

Code

Paper: https://arxiv.org/abs/1704.02510

Example

$ cd dualgan/
$ python3 dualgan.py

GAN

Implementation of Generative Adversarial Network with a MLP generator and discriminator.

Code

Paper: https://arxiv.org/abs/1406.2661

Example

$ cd gan/
$ python3 gan.py

InfoGAN

Implementation of InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.

Code

Paper: https://arxiv.org/abs/1606.03657

Example

$ cd infogan/
$ python3 infogan.py

LSGAN

Implementation of Least Squares Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1611.04076

Example

$ cd lsgan/
$ python3 lsgan.py

Pix2Pix

Implementation of Image-to-Image Translation with Conditional Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1611.07004

Example

$ cd pix2pix/
$ bash download_dataset.sh facades
$ python3 pix2pix.py

PixelDA

Implementation of Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks.

Code

Paper: https://arxiv.org/abs/1612.05424

MNIST to MNIST-M Classification

Trains a classifier on MNIST images that are translated to resemble MNIST-M (by performing unsupervised image-to-image domain adaptation). This model is compared to the naive solution of training a classifier on MNIST and evaluating it on MNIST-M. The naive model manages a 55% classification accuracy on MNIST-M while the one trained during domain adaptation gets a 95% classification accuracy.

$ cd pixelda/
$ python3 pixelda.py
Method Accuracy
Naive 55%
PixelDA 95%

SGAN

Implementation of Semi-Supervised Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1606.01583

Example

$ cd sgan/
$ python3 sgan.py

SRGAN

Implementation of Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

Code

Paper: https://arxiv.org/abs/1609.04802

Example

$ cd srgan/
<follow steps at the top of srgan.py>
$ python3 srgan.py

WGAN

Implementation of Wasserstein GAN (with DCGAN generator and discriminator).

Code

Paper: https://arxiv.org/abs/1701.07875

Example

$ cd wgan/
$ python3 wgan.py

WGAN GP

Implementation of Improved Training of Wasserstein GANs.

Code

Paper: https://arxiv.org/abs/1704.00028

Example

$ cd wgan_gp/
$ python3 wgan_gp.py


DCGAN(unsupervised representation learning with deep convolutional generative adv)的实现
GAN一个开发者的理解-写的很棒
万事OK自学网:51自学网_软件自学网_CAD自学网自学excel、自学PS、自学CAD、自学C语言、自学css3实例,是一个通过网络自主学习工作技能的自学平台,网友喜欢的软件自学网站。