Updating documentation, adding bibtex, fixing typo.

PiperOrigin-RevId: 253420156

robust_loss/README.md

@@ -4,14 +4,24 @@ This directory contains reference code for the paper
 [A General and Adaptive Robust Loss Function](https://arxiv.org/abs/1701.03077),
 Jonathan T. Barron CVPR, 2019
 
-The code is implemented in Tensorflow and the required packages are listed in
-`requirements.txt`.
-
-If you'd like this loss, include `general.py` or `adaptive.py` and call the loss
+To use this code, include `general.py` or `adaptive.py` and call the loss
 function. `general.py` implements the "general" form of the loss, which assumes
 you are prepared to set and tune hyperparameters yourself, and `adaptive.py`
 implements the "adaptive" form of the loss, which tries to adapt the
 hyperparameters automatically and also includes support for imposing losses in
 different image representations. The probability distribution underneath the
-adaptive loss is implemented in `distribution.py`. Demo code for training
-different variants of a VAE on Celeb-A as was done in the paper is in `vae.py`.
+adaptive loss is implemented in `distribution.py`.
+
+This code repository is shared with all of Google Research, so it's not very
+useful for reporting or tracking bugs. If you have any issues using this code,
+please do not open an issue, and instead just email [email protected].
+
+If you use this code, please cite it:
+```
+@article{BarronCVPR2019,
+  Author = {Jonathan T. Barron},
+  Title = {A General and Adaptive Robust Loss Function},
+  Journal = {CVPR},
+  Year = {2019}
+}
+```