**ACGAN**| [Arxiv](https://arxiv.org/abs/1610.09585) | 
**EBGAN**| [Arxiv](https://arxiv.org/abs/1609.03126) | 
-**BEGAN**| [Arxiv](https://arxiv.org/abs/1702.08431) | 
+**BEGAN**| [Arxiv](https://arxiv.org/abs/1702.08431) | 
### Results for mnist
-Network architecture of generator and discriminator is the exaclty sames as in [infoGAN paper](https://arxiv.org/abs/1606.03657).
+Network architecture of generator and discriminator is the exaclty sames as in [infoGAN paper](https://arxiv.org/abs/1606.03657).
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.
-The following results can be reproduced with command:
+The following results can be reproduced with command:
```
python main.py --dataset mnist --gan_type 
| 
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.
+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
@@ -122,7 +122,7 @@ infoGAN tends to ignore noise-vector. It results in that various style within th
**VAE**| [Arxiv](https://arxiv.org/abs/1312.6114) | 
**CVAE**| [Arxiv](https://arxiv.org/abs/1406.5298) | 
**DVAE**| [Arxiv](https://arxiv.org/abs/1511.06406) | (to be added)
-**AAE**| [Arxiv](https://arxiv.org/abs/1511.05644) | (to be added)
+**AAE**| [Arxiv](https://arxiv.org/abs/1511.05644) | (to be added)
#### Variants of VAE structure
@@ -130,7 +130,7 @@ infoGAN tends to ignore noise-vector. It results in that various style within th
### Results for mnist
Network architecture of decoder(generator) and encoder(discriminator) is the exaclty sames as in [infoGAN paper](https://arxiv.org/abs/1606.0365). The number of output nodes in encoder is different. (2x z_dim for VAE, 1 for GAN)
-The following results can be reproduced with command:
+The following results can be reproduced with command:
```
python main.py --dataset mnist --gan_type 
| 
| 
### Results for fashion-mnist
-Comments on network architecture in mnist are also applied to here.
+Comments on network architecture in mnist are also applied to here.
-The following results can be reproduced with command:
+The following results can be reproduced with command:
```
python main.py --dataset fashion-mnist --gan_type
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