Tensorflow models with simple ways

Some models with easy understanding code,it will help you understand what the model does.These models can only work with not very good results.

Models list

1.Adversarial Networks (GAN)

• AAE   Adversarial Autoencoders
  
• ACGAN   Conditional Image Synthesis With Auxiliary Classifier GANs
  
• Auto encoder  Recent Advances in Autoencoder-Based Representation Learning
  
• BGAN   Boundary-Seeking Generative Adversarial Networks
  
• BiGAN   Bidirectional Generative Adversarial Network
  
• CCGAN   Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
  
• CGAN   Conditional Generative Adversarial Nets
  
• CoGAN   Coupled generative adversarial networks
  
• CycleGAN   Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
  
• DCGAN   Deep Convolutional Generative Adversarial Network
  
• GAN   Generative Adversarial Network with a MLP generator and discriminator
  
• VAE   Auto-Encoding Variational Bayes
  

2.Special structure Convolutional network (Not recurring paper)

• DenseNet   Densely Connected Convolutional Networks
  
• ResNet   Deep Residual Learning for Image Recognition
  
• HighwayNet   Highway Networks
  
• MobileNet_v1&v2   MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
  
                                  MobileNetV2: Inverted Residuals and Linear Bottlenecks
  

3.Other Models

• None
  

Results of some models

The results are based on MNIST or Fashion-MNIST

AAE

ACGAN

Auto_encoder

Input image → Hidden layer → Output image

BGAN

BiGAN

CCGAN

Real image → Random cropping image → Repaired image

CGAN

CoGAN

A: MNIST      B: Rotate 90 degrees MNIST

The model try to convert between A and B.

A → B



B → A

CycleGAN

A: MNIST      B: Rotate 90 degrees MNIST

The model try to convert between A and B.

A → B



B → A

DCGAN

GAN

VAE

Input image → Hidden layer → Output image