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.
pip install git+https://github.com/jonbarron/robust_loss_pytorch
git clone https://github.com/jonbarron/robust_loss_pytorch
cd robust_loss_pytorch/
pip install -e .[dev]
Tests can then be run from the root of the project using:
nosetests
To use this code import lossfun, or AdaptiveLossFunction 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.
from robust_loss_pytorch import lossfun
or
from robust_loss_pytorch import AdaptiveLossFunction
A toy example of how this code can be used is in example.ipynb.
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}
}