message: Remove broken link.
diff: @@ -51,9 +51,10 @@ network to evaluate your unconditional generative model. You can also also use
  your own pretrained classifier for more specific performance numbers, or use
  other methods for evaluating conditional generative models.
  -* [examples](https://github.com/tensorflow/models/tree/master/gan/):
  +* examples (coming soon):
  See examples of how to use TFGAN to make GAN training easier, or use the more complicated examples to jumpstart your
  -own project.
  +own project. These include unconditional and conditional GANs, InfoGANs,
  +adversarial losses on existing networks, and image-to-image translation.
  ## Training a GAN model
  