Generative Adversarial Networks (GANs)
Lists
Name | Paper Link | Value Function |
---|---|---|
GAN | Arxiv | |
LSGAN | Arxiv | |
WGAN | Arxiv | |
WGAN-GP | Arxiv | |
DRAGAN | Arxiv | |
CGAN | Arxiv | |
infoGAN | Arxiv | |
ACGAN | Arxiv | |
EBGAN | Arxiv | |
BEGAN | Arxiv |
Variants of GAN structure
Results for mnist
Network architecture of generator and discriminator is the exaclty sames as in infoGAN paper.
For fair comparison of core ideas in all gan variants, all implementations for network architecture are kept same except EBGAN and BEGAN. Small modification is made for EBGAN/BEGAN, since those adopt auto-encoder strucutre for discriminator. But I tried to keep the capacity of discirminator.
Random generation
All results are randomly sampled.
Name | Epoch 2 | Epoch 10 | Epoch 25 |
---|---|---|---|
GAN | |||
LSGAN | |||
WGAN | |||
WGAN-GP | |||
DRAGAN | |||
EBGAN | |||
BEGAN |
Conditional generation
Each row has the same noise vector and each column has the same label condition.
Name | Epoch 1 | Epoch 10 | Epoch 25 |
---|---|---|---|
CGAN | |||
ACGAN | |||
infoGAN |
InfoGAN : Manipulating two continous codes
Results for fashion-mnist
Comments on network architecture in mnist are also applied to here.
Fashion-mnist is a recently proposed dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. (T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle boot)
Random generation
All results are randomly sampled.
Name | Epoch 1 | Epoch 20 | Epoch 40 |
---|---|---|---|
GAN | |||
LSGAN | |||
WGAN | |||
WGAN-GP | |||
DRAGAN | |||
EBGAN | |||
BEGAN |
Conditional generation
Each row has the same noise vector and each column has the same label condition.
Name | Epoch 1 | Epoch 20 | Epoch 40 |
---|---|---|---|
CGAN | |||
ACGAN | |||
infoGAN |
Without hyper-parameter tuning from mnist-version, ACGAN/infoGAN does not work well as compared with CGAN.
ACGAN tends to fall into mode-collapse.
infoGAN tends to ignore noise-vector. It results in that various style within the same class can not be represented.
InfoGAN : Manipulating two continous codes
Some results for celebA
(to be added)
Variational Auto-Encoders (VAEs)
Lists
Name | Paper Link | Loss Function |
---|---|---|
VAE | Arxiv | |
CVAE | Arxiv | |
DVAE | Arxiv | (to be added) |
AAE | Arxiv | (to be added) |
Variants of VAE structure
Results for mnist
Network architecture of decoder(generator) and encoder(discriminator) is the exaclty sames as in infoGAN paper. The number of output nodes in encoder is different. (2x z_dim for VAE, 1 for GAN)
Random generation
All results are randomly sampled.
Name | Epoch 1 | Epoch 10 | Epoch 25 |
---|---|---|---|
VAE | |||
GAN |
Results of GAN is also given to compare images generated from VAE and GAN.
The main difference (VAE generates smooth and blurry images, otherwise GAN generates sharp and artifact images) is cleary observed from the results.
Conditional generation
Each row has the same noise vector and each column has the same label condition.
Name | Epoch 1 | Epoch 10 | Epoch 25 |
---|---|---|---|
CVAE | |||
CGAN |
Results of CGAN is also given to compare images generated from CVAE and CGAN.
Learned manifold
The following results can be reproduced with command:
Please notice that dimension of noise-vector z is 2.
Name | Epoch 1 | Epoch 10 | Epoch 25 |
---|---|---|---|
VAE |
Results for fashion-mnist
Comments on network architecture in mnist are also applied to here.
The following results can be reproduced with command:
Random generation
All results are randomly sampled.
Name | Epoch 1 | Epoch 20 | Epoch 40 |
---|---|---|---|
VAE | |||
GAN |
Results of GAN is also given to compare images generated from VAE and GAN.
Conditional generation
Each row has the same noise vector and each column has the same label condition.
Name | Epoch 1 | Epoch 20 | Epoch 40 |
---|---|---|---|
CVAE | |||
CGAN |
Results of CGAN is also given to compare images generated from CVAE and CGAN.
Learned manifold
The following results can be reproduced with command:
Please notice that dimension of noise-vector z is 2.
Name | Epoch 1 | Epoch 10 | Epoch 25 |
---|---|---|---|
VAE |