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Tags: #gan

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

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

CoGAN: Learning joint distribution with GAN
Original GAN and Conditional GAN are for learning marginal and conditional distribution of data respectively. But how can we extend ...

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

InfoGAN: unsupervised conditional GAN in TensorFlow and Pytorch
Adding Mutual Information regularization to a GAN turns out gives us a very nice effect: learning data representation and its ...

Conditional Generative Adversarial Nets in TensorFlow
Having seen GAN, VAE, and CVAE model, it is only proper to study the Conditional GAN model next!

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