Learning-with-Noisy-Labels

A curated list of most recent papers & codes in Learning with Noisy Labels

Some recent works about group-distributional robustness, label distribution shifts, are also included.

Public Software

Docta-AI: An advanced data-centric AI platform that detects and rectifies issues in any data format (i.e., label error detection). [Website]

Competition

A Hands-on Tutorial for Learning with Noisy Labels (IJCAI 2022)[website]

Tutorial

1st Learning and Mining with Noisy Labels Challenge (IJCAI 2023)[Website][GitHub]

Content

Benchmarks & Leaderboard
Papers & Code in 2023
Papers & Code in 2022
Papers & Code in 2021
Papers & Code in 2020
- ICML 2020
- ICLR 2020
- Nips 2020
- AAAI 2020
- CVPR 2020
- ECCV 2020
- ArXiv 2020

Benchmarks & Leaderboard

Real-world noisy-label bechmarks:

Dataset	Leaderboard Link	Website	Paper
CIFAR-10N	[Leaderboard]	[Website]	[Paper]
CIFAR-100N	[Leaderboard]	[Website]	[Paper]
Red Stanford Cars	N/A	[Website]	[Paper]
Red Mini-ImageNet	N/A	[Website]	[Paper]
Animal-10N	[Leaderboard]	[Website]	[Paper]
Food-101N	N/A	[Website]	[Paper]
Clothing1M	[Leaderboard]	[Website]	[Paper]

Simulation of label noise: An Instance-Dependent Simulation Framework for Learning with Label Noise. [Paper]

This repo focus on papers after 2019, for previous works, please refer to (https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise).

Papers & Code in 2023

KDD 2023

[UCSC REAL Lab] To Aggregate or Not? Learning with Separate Noisy Labels. [Paper]
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling. [Paper][Code]
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction. [Paper]
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler. [Paper][Code]
Complementary Classifier Induced Partial Label Learning. [Paper][Code]
Partial-label Learning with Mixed Closed-Set and Open-Set Out-of-Candidate Examples. [Paper]
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers. [Paper][Code]

NeurIPS 2023

The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning. [Paper][Code]
AQuA: A Benchmarking Tool for Label Quality Assessment. [Paper]
Efficient Testable Learning of Halfspaces with Adversarial Label Noise. [Paper]
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data. [Paper][Code]
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy. [Paper]
Subclass-Dominant Label Noise: A Counterexample for the Success of Early Stopping. [Paper][Code]
Label Correction of Crowdsourced Noisy Annotations with an Instance-Dependent Noise Transition Model. [Paper]
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels. [Paper][Code]
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams. [Paper][Code]
Active Negative Loss Functions for Learning with Noisy Labels. [Paper][Code]
Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels. [Paper][Code]
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign? [Paper]
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels. [Paper][Code]
Deep Insights into Noisy Pseudo Labeling on Graph Data. [Paper]
ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections. [Paper][Code]
ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning. [Paper][Code]
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping. [Paper][Code]
Label Poisoning is All You Need. [Paper][Code]
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples. [Paper]
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers. [Paper]
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text. [Paper][Code]

ICML 2023

[UCSC REAL Lab] Identifiability of Label Noise Transition Matrix. [Paper]
Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise? [Paper]
Mitigating Memorization of Noisy Labels by Clipping the Model Prediction. [Paper][Code]
CrossSplit: Mitigating Label Noise Memorization through Data Splitting. [Paper][Code]
Understanding Self-Distillation in the Presence of Label Noise. [Paper]
RandomClassificationNoisedoesnotdefeatAllConvexPotentialBoosters IrrespectiveofModelChoice. [Paper]
Deep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach. [Paper]
Delving into Noisy Label Detection with Clean Data. [Paper]
When does Privileged information Explain Away Label Noise? [Paper]
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective. [Paper][Code]
Promises and Pitfalls of Threshold-based Auto-labeling. [Paper]
Accelerating Exploration with Unlabeled Prior Data. [Paper]

CVPR 2023

Twin Contrastive Learning with Noisy Labels. [Paper][Code]
Exploring High-Quality Pseudo Masks for Weakly Supervised Instance Segmentation. [Paper][Code]
HandsOff: Labeled Dataset Generation with No Additional Human Annotations. [Paper][Code]
Learning from Noisy Labels with Decoupled Meta Label Purifier. [Paper][Code]
DISC: Learning from Noisy Labels via Dynamic Instance-Specific Selection and Correction. [Paper][Code]
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels. [Paper]
Fine-Grained Classification with Noisy Labels. [Paper]
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies. [Paper][Code]
MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-supervised Object Detection. [Paper][Code]
OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels. [Paper]
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection. [Paper][Code]
Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module. [Paper][Code]
Learning with Noisy labels via Self-supervised