noisy-labels

subeeshvasu/Awesome-Learning-with-Label-Noise

A curated list of resources for Learning with Noisy Labels

Renumics/awesome-open-data-centric-ai

Curated list of open source tooling for data-centric AI on unstructured data.

weijiaheng/Advances-in-Label-Noise-Learning

A curated (most recent) list of resources for Learning with Noisy Labels

encord-team/encord-active

The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.

shengliu66/ELR

Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels

xjtushujun/meta-weight-net

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

YisenWang/symmetric_cross_entropy_for_noisy_labels

Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"

huangyangyu/NoiseFace

Noise-Tolerant Paradigm for Training Face Recognition CNNs [Official, CVPR 2019]

HanxunH/Active-Passive-Losses

[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels

chengtan9907/Co-learning

The official implementation of the ACM MM'21 paper Co-learning: Learning from noisy labels with self-supervision.

UCSC-REAL/negative-label-smoothing

[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"

ydkim1293/NLNL-Negative-Learning-for-Noisy-Labels

NLNL: Negative Learning for Noisy Labels

chenpf1025/noisy_label_understanding_utilizing

ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

salesforce/MoPro

MoPro: Webly Supervised Learning

weijiaheng/Robust-f-divergence-measures

[ICLR2021] Official Pytorch implementation of "When Optimizing f-Divergence is Robust with Label noise"

Kangningthu/ADELE

Adaptive Early-Learning Correction for Segmentation from Noisy Annotations (CVPR 2022 Oral)

zhangchbin/OnlineLabelSmoothing

The official code for the paper "Delving Deep into Label Smoothing", IEEE TIP 2021

moucheng2017/Learn_Noisy_Labels_Medical_Images

[NeurIPS 2020] Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

Hzzone/TCL

Twin Contrastive Learning with Noisy Labels (CVPR 2023)

ContrastToDivide/C2D

PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

RyanWangZf/Influence_Subsampling

Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020

ayaanzhaque/SDCNL

Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction (ICANN 2021)

nazmul-karim170/UNICON-Noisy-Label

[CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"

XinshaoAmosWang/ProSelfLC-AT

noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

penghu-cs/MRL

Learning Cross-Modal Retrieval with Noisy Labels (CVPR 2021, PyTorch Code)

XLearning-SCU/2021-CVPR-MvCLN

PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

cgnorthcutt/confidentlearning-reproduce

Official data release to reproduce Confident Learning paper results

chenpf1025/IDN

AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

cxliu0/OA-MIL

[ECCV 2022] Robust Object Detection With Inaccurate Bounding Boxes

arghosh/noisy_label_pretrain

bupt-ai-cz/PGDF

Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

MrChenFeng/SSR_BMVC2022

SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise (BMVC2022)

XLearning-SCU/Awesome-Noisy-Correspondence

This is a summary of research on noisy correspondence. There may be omissions. If anything is missing please get in touch with us. Our emails: linyijie.gm@gmail.com yangmouxing@gmail.com qinyang.gm@gmail.com

VirtuosoResearch/Regularized-Self-Labeling

A regularized self-labeling approach to improve the generalization and robustness of fine-tuned models

wangjksjtu/rl-perturbed-reward

Reinforcement Learning with Perturbed Reward, AAAI 2020