Tags: #gan

Training GAN by moving the generated samples to the decision boundary.

2017 is the year GAN loss its logarithm. First, it was Wasserstein GAN, and now, it's LSGAN's turn.

Original GAN and Conditional GAN are for learning marginal and conditional distribution of data respectively. But how can we extend ...

Wasserstein GAN comes with promise to stabilize GAN training and abolish mode collapse problem in GAN.

Adding Mutual Information regularization to a GAN turns out gives us a very nice effect: learning data representation and its ...

Having seen GAN, VAE, and CVAE model, it is only proper to study the Conditional GAN model next!

Let's try to implement Generative Adversarial Nets (GAN), first introduced by Goodfellow et al, 2014, with TensorFlow. We'll use MNIST ...