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PiperOrigin-RevId: 204744158
vitalyvels · Jul 16, 2018 · 81ba388 · 81ba388
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diff --git a/compare_gan/src/tfhub_models.ipynb b/compare_gan/src/tfhub_models.ipynb
@@ -73,7 +73,7 @@
         "\n",
         "This Colab notebook shows how to use a collection of pre-trained generative adversarial network models (GANs) for CIFAR10, CelebA HQ (128x128) and LSUN bedroom datasets to generate images. Random vectors are fed into the latent space to generate RGB images using the pre-trained generators.\n",
         "\n",
-        "For the details of the setup, please refer to our paper: [The GAN Landscape: Losses, Architectures, Regularization, and Normalization](https://openreview.net/pdf?id=SkeFUsQNx7).\n",
+        "For the details of the setup, please refer to our paper: [The GAN Landscape: Losses, Architectures, Regularization, and Normalization](https://arxiv.org/abs/1807.04720).\n",
         "\n",
         "The code used to train these models is available at: https://github.com/google/compare_gan\n",
         "\n",

diff --git a/setup.py b/setup.py
@@ -31,10 +31,10 @@
     package_data={
     },
     scripts=[
-        'bin/compare_gan_generate_tasks',
-        'bin/compare_gan_prepare_datasets.sh',
-        'bin/compare_gan_run_one_task',
-        'bin/compare_gan_run_test.sh',
+        'compare_gan/bin/compare_gan_generate_tasks',
+        'compare_gan/bin/compare_gan_prepare_datasets.sh',
+        'compare_gan/bin/compare_gan_run_one_task',
+        'compare_gan/bin/compare_gan_run_test.sh',
     ],
     install_requires=[
         'future',