ReLoss - Official implementation for paper "Relational Surrogate Loss Learning" ICLR 2022

Related tags

Deep Learningpytorchlosssurrogateiclr2022relosssurrogate-loss
Overview

Relational Surrogate Loss Learning (ReLoss)

Official implementation for paper "Relational Surrogate Loss Learning" in International Conference on Learning Representations (ICLR) 2022.

By Tao Huang, Zekang Li, Hua Lu, Yong Shan, Shusheng Yang, Yang Feng, Fei Wang, Shan You, Chang Xu.

Usage

Install ReLoss

pip install git+https://github.com/hunto/ReLoss.git

Or install for development:

git clone https://github.com/hunto/ReLoss
cd ReLoss
pip install -e .

Training with ReLoss

All the inputs and outputs of ReLoss are the same as the original loss.

  • classification 
    from reloss.cls import ReLoss
loss_fn = ReLoss()
  • human pose estimation 
    from reloss.pose import ReLoss
loss_fn = ReLoss(heatmap_size=(64, 48))

Citation

@inproceedings{
huang2022relational,
title={Relational Surrogate Loss Learning},
author={Tao Huang and Zekang Li and Hua Lu and Yong Shan and Shusheng Yang and Yang Feng and Fei Wang and Shan You and Chang Xu},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=dZPgfwaTaXv}
}
Owner
Tao Huang
Machine Learning & Computer Vision
Tao Huang
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