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

Last update: Nov 22, 2022

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}
}

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Comments

train_reloss.py is not runnable

Hi, Thanks for sharing your awesome work. I notice there are many typos and un-existed parameters in train_reloss.py, such as the code from line 33-40. 'i' is not defined, and 'logits, targets' from the corresponding batch are not used in the following code.

opened by lwmlyy 12
Update spearman.py

In https://arxiv.org/abs/2203.09630, we propose monotonic differentiable sorting networks which perform better than logistic with ART. Thus, we changed the default interpolation_type to cauchy in our recent update to diffsort. While cauchy might work better, I suggest specifying the interpolation type explicitly for reproducibility.

Best regards,

Felix

opened by Felix-Petersen 2

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

Related tags

Overview

Relational Surrogate Loss Learning (ReLoss)

Usage

Install ReLoss

Training with ReLoss

Citation

You might also like...

[ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

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Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

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The official implementation of NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021]. https://arxiv.org/pdf/2101.12378.pdf

Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.

Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Official Implementation of 'UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers' ICLR 2021(spotlight)

Comments

train_reloss.py is not runnable

Update spearman.py

Releases(v1.0.0)

v1.0.0(Feb 18, 2022)

Owner

Tao Huang

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