aissatoupaye/gan-yhat

GAN implementations

gan-yhat

GAN implementations with PyTorch

The followings are a list of GAN implemented here.

1. Vanilla GAN
   • Vanilla GAN for MNIST
   • Vanilla GAN for Fashion MNIST
2. Conditional GAN for MNIST
3. DCGAN for Fashion MNIST
4. Improved GAN
   • Feature Maching
5. AnoGAN

Vanilla GAN

MNIST

It shows a little bit of mode collapse; a commonly encountered failure case for GANs where the generator produces samples with extremely low variety. In this case, the generator produces 1 with extremely high probability.

Conditional GAN

Conditional GAN reduces mode collapse issue by giving the model additional information.

DCGAN

DCGAN makes use of convolutions and transposed convolutions.

RNN GAN

Sin

It uses feature mapping and LSTM.

Improved GAN

Feature Matching

AnoGAN

Anomaly Detection with GAN

images generated by GAN Generator

The following images are generated by the GAN Generator. It is the same model as the DCGAN model.

Anomaly Detection for Normal Data

The following images are normal images.

It is good if red dots are less shown.

Anomaly Detection for Abnormal Data