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@@ -10,6 +10,7 @@ Tl;dr GANs contain two networks: a generator and a discriminator. The generator
 + DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1511.06434), [[github]](https://github.com/carpedm20/DCGAN-tensorflow)
 + WGAN: Wasserstein GAN, [[paper]](https://arxiv.org/abs/1701.07875), [[github]](https://github.com/martinarjovsky/WassersteinGAN)
 + WGAN-GP: Improved Training of Wasserstein GANs, [[paper]](https://arxiv.org/pdf/1704.00028.pdf), [[github]](https://github.com/caogang/wgan-gp)
++ RGAN: The relativistic discriminator: a key element missing from standard GAN, [[paper]](https://arxiv.org/abs/1807.00734), [[github]](https://github.com/AlexiaJM/RelativisticGAN)
 
 ### Conditional GANs
 + CGAN: Conditional Generative Adversarial Nets, [[paper]](https://arxiv.org/abs/1411.1784), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/cgan/cgan.py)
@@ -18,6 +19,11 @@ Tl;dr GANs contain two networks: a generator and a discriminator. The generator
 ### Image-to-Image Translation
 + CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, [[paper]](https://arxiv.org/abs/1703.10593), [[github]](https://github.com/junyanz/CycleGAN)
 + StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1711.09020), [[github]](https://github.com/yunjey/stargan)
++ Pix2Pix: Image-to-Image Translation with Conditional Adversarial Nets, [[paper]](https://arxiv.org/abs/1611.07004), [[github]](https://github.com/phillipi/pix2pix)
+
+### Volumetric (3D) Generation
++ 3DGAN: Learning a Probabilistic Latent Space of Object Shapes
+via 3D Generative-Adversarial Modeling, [[paper]](http://3dgan.csail.mit.edu/papers/3dgan_nips.pdf), [[github]](https://github.com/enochkan/3dgan-keras)
 
 ## Applications using GANs