This directory contains reference code for the paper A General and Adaptive Robust Loss Function, Jonathan T. Barron CVPR, 2019
The code is implemented in Pytorch, and is a port of the TensorFlow
implementation at:
https://github.com/google-research/google-research/tree/master/robust_loss.
The required packages are listed in requirements.txt.
If you'd like this loss, 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.