# robust_loss_pytorch

**Repository Path**: stormvrar/robust_loss_pytorch

## Basic Information

- **Project Name**: robust_loss_pytorch
- **Description**: A pytorch port of google-research/google-research/robust_loss/
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-26
- **Last Updated**: 2021-02-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# A General and Adaptive Robust Loss Function

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 Pytorch, and is a port of the TensorFlow
implementation at:
https://github.com/google-research/google-research/tree/master/robust_loss.

## Installation

### Typical Install
```
pip install git+https://github.com/jonbarron/robust_loss_pytorch
```

### Development
```
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
```

## Usage

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`.

## Citation

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}
}
```