This directory contains Tensorflow 2 reference code for the paper A General and Adaptive Robust Loss Function, Jonathan T. Barron CVPR, 2019

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.

The VAE experiment from the paper can be reproduced by running vae.py. See example.ipynb for a simple toy example of how this loss can be used.

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}
}