ReLoss - Official implementation for paper "Relational Surrogate Loss Learning" ICLR 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}
}
You might also like...
[ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
[ICLR 2022 Oral] F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

F8Net Fixed-Point 8-bit Only Multiplication for Network Quantization (ICLR 2022 Oral) OpenReview | arXiv | PDF | Model Zoo | BibTex PyTorch implementa

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

DAB-DETR This is the official pytorch implementation of our ICLR 2022 paper DAB-DETR. Authors: Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi

Official code for the ICLR 2021 paper Neural ODE Processes
Official code for the ICLR 2021 paper Neural ODE Processes

Neural ODE Processes Official code for the paper Neural ODE Processes (ICLR 2021). Abstract Neural Ordinary Differential Equations (NODEs) use a neura

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

Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology Sharon Zhou, Eric Zelikman

Official codebase for ICLR oral paper Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling

CLIORA This is the official codebase for ICLR oral paper: Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling. We introduce

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
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

NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021] Release Notes The offical PyTorch implementation of NeMo, p

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

SuperGAT Official implementation of Self-supervised Graph Attention Networks (SuperGAT). This model is presented at How to Find Your Friendly Neighbor

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

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets This is the official PyTorch implementation for the paper Rapid Neural A

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

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

Comments
  • train_reloss.py is not runnable

    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

    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
Owner
Tao Huang
Machine Learning & Computer Vision
Tao Huang
A graph-to-sequence model for one-step retrosynthesis and reaction outcome prediction.

Graph2SMILES A graph-to-sequence model for one-step retrosynthesis and reaction outcome prediction. 1. Environmental setup System requirements Ubuntu:

29 Nov 18, 2022
Weakly Supervised Posture Mining with Reverse Cross-entropy for Fine-grained Classification

Fine-grainedImageClassification Weakly Supervised Posture Mining with Reverse Cross-entropy for Fine-grained Classification We trained model here: lin

ZhenchaoTang 14 Oct 21, 2022
An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners This is a coarse version for MAE, only make the pretrain model, the fine

FlyEgle 214 Dec 29, 2022
Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.

Multi-Time Attention Networks (mTANs) This repository contains the PyTorch implementation for the paper Multi-Time Attention Networks for Irregularly

The Laboratory for Robust and Efficient Machine Learning 68 Dec 17, 2022
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Bitcoin Realized Volatility Forecasting with GARCH and Multivariate LSTM Author: Chi Bui This Repository Repository Directory ├── README.md

Chi Bui 113 Dec 29, 2022
Pytorch implementation for "Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion" (NeurIPS 2021)

Density-aware Chamfer Distance This repository contains the official PyTorch implementation of our paper: Density-aware Chamfer Distance as a Comprehe

Tong WU 93 Dec 15, 2022
Training data extraction on GPT-2

Training data extraction from GPT-2 This repository contains code for extracting training data from GPT-2, following the approach outlined in the foll

Florian Tramer 62 Dec 07, 2022
An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicity.

Fast Face Classification (F²C) This is the code of our paper An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicit

33 Jun 27, 2021
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Emma 1 Jan 18, 2022
Official implementation for paper: A Latent Transformer for Disentangled Face Editing in Images and Videos.

A Latent Transformer for Disentangled Face Editing in Images and Videos Official implementation for paper: A Latent Transformer for Disentangled Face

InterDigital 108 Dec 09, 2022
Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

Graph Convolutional Networks for Hyperspectral Image Classification Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot T

Danfeng Hong 154 Dec 13, 2022
An Implementation of SiameseRPN with Feature Pyramid Networks

SiameseRPN with FPN This project is mainly based on HelloRicky123/Siamese-RPN. What I've done is just add a Feature Pyramid Network method to the orig

3 Apr 16, 2022
Advanced Signal Processing Notebooks and Tutorials

Advanced Digital Signal Processing Notebooks and Tutorials Prof. Dr. -Ing. Gerald Schuller Jupyter Notebooks and Videos: Renato Profeta Applied Media

Guitars.AI 115 Dec 13, 2022
Analysis of Antarctica sequencing samples contaminated with SARS-CoV-2

Analysis of SARS-CoV-2 reads in sequencing of 2018-2019 Antarctica samples in PRJNA692319 The samples analyzed here are described in this preprint, wh

Jesse Bloom 4 Feb 09, 2022
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps

Range Image-based 3D LiDAR Localization This repo contains the code for our ICRA2021 paper: Range Image-based LiDAR Localization for Autonomous Vehicl

Photogrammetry & Robotics Bonn 208 Dec 15, 2022
A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning

A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning Website • About • Installation • Using OpenDR

OpenDR 304 Dec 28, 2022
Pytorch Implementation for CVPR2018 Paper: Learning to Compare: Relation Network for Few-Shot Learning

LearningToCompare Pytorch Implementation for Paper: Learning to Compare: Relation Network for Few-Shot Learning Howto download mini-imagenet and make

Jackie Loong 246 Dec 19, 2022
Deep learning for Engineers - Physics Informed Deep Learning

SciANN: Neural Networks for Scientific Computations SciANN is a Keras wrapper for scientific computations and physics-informed deep learning. New to S

SciANN 195 Jan 03, 2023
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation (CVPR2019) This is a pytorch implementatio

Yawei Luo 280 Jan 01, 2023
Code for Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games

Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games How to run our algorithm? Create the new environment using: conda

MARL @ SJTU 8 Dec 27, 2022