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transporter/README.md

@@ -1,7 +1,9 @@
 # Transporter - Unsupervised Learning of Object Keypoints for Perception and Control
 
 This directory contains a [Sonnet](https://sonnet.dev) implementation of
-the Transporter architecture.
+the Transporter architecture and a notebook explaining how the model can be used
+for keypoint inference. To launch the notebook in Google colab, [click here](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/transporter/transporter_example.ipynb).
+
 
 The Transporter is a neural network architecture for discovering concise
 geometric object representations in terms of keypoints or image-space
@@ -13,6 +15,9 @@ across long time-horizons more accurately than recent similar methods.
 For details, see our
 paper [Unsupervised Learning of Object Keypoints for Perception and Control](https://arxiv.org/abs/1906.11883).
 
+If you use the code here please cite this paper.
+> Tejas Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, Volodymyr Mnih.  *Unsupervised Learning of Object Keypoints for Perception and Control*.  NeurIPS 2019.  [\[arXiv\]](https://arxiv.org/abs/1906.11883).
+
 ## Contributors
 * Tejas Kulkarni <[email protected]>
 * Ankush Gupta <[email protected]>
@@ -23,3 +28,6 @@ paper [Unsupervised Learning of Object Keypoints for Perception and Control](htt
 * Volodymyr Mnih
 
 
+## Disclaimer
+This is not an official Google product.
+