Updated broken link

site/en/tutorials/generative/pix2pix.ipynb

@@ -72,7 +72,7 @@
       "source": [
         "This tutorial demonstrates how to build and train a conditional generative adversarial network (cGAN) called pix2pix that learns a mapping from input images to output images, as described in [Image-to-image translation with conditional adversarial networks](https://arxiv.org/abs/1611.07004){:.external} by Isola et al. (2017). pix2pix is not application specific—it can be applied to a wide range of tasks, including synthesizing photos from label maps, generating colorized photos from black and white images, turning Google Maps photos into aerial images, and even transforming sketches into photos.\n",
         "\n",
-        "In this example, your network will generate images of building facades using the [CMP Facade Database](http://cmp.felk.cvut.cz/~tylecr1/facade/) provided by the [Center for Machine Perception](http://cmp.felk.cvut.cz/){:.external} at the [Czech Technical University in Prague](https://www.cvut.cz/){:.external}. To keep it short, you will use a [preprocessed copy](https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/){:.external} of this dataset created by the pix2pix authors.\n",
+        "In this example, your network will generate images of building facades using the [CMP Facade Database](http://cmp.felk.cvut.cz/~tylecr1/facade/) provided by the [Center for Machine Perception](http://cmp.felk.cvut.cz/){:.external} at the [Czech Technical University in Prague](https://www.cvut.cz/){:.external}. To keep it short, you will use a [preprocessed copy](https://efrosgans.eecs.berkeley.edu/pix2pix/datasets/){:.external} of this dataset created by the pix2pix authors.\n",
         "\n",
         "In the pix2pix cGAN, you condition on input images and generate corresponding output images. cGANs were first proposed in [Conditional Generative Adversarial Nets](https://arxiv.org/abs/1411.1784) (Mirza and Osindero, 2014)\n",
         "\n",