Learning-with-Noisy-Labels

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

Content
- Papers & Code in 2021
- Papers & Code in 2020
  - ICML 2020
  - ICLR 2020
  - Nips 2020
  - AAAI 2020
  - CVPR 2020
  - ECCV 2020
  - ArXiv 2020

Papers & Code in 2021

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

ICLR 2021

Conference date: May 3, 2021 -- May 7, 2021

[UCSC REAL Lab] When Optimizing f-Divergence is Robust with Label Noise. [Paper][Code]
- Poster Session 3: May 3, 2021, 5 p.m. -- May 3, 2021, 7 p.m. (PDT)
[UCSC REAL Lab] Learning with Instance-Dependent Label Noise: A Sample Sieve Approach. [Paper][Code]
- Poster Session 9: May 5, 2021, 5 p.m. -- May 5, 2021, 7 p.m. (PDT)
Noise against noise: stochastic label noise helps combat inherent label noise. [Paper][Code]
- Poster Session 1: May 3, 2021, 1 a.m. -- May 3, 2021, 3 a.m. (PDT)
Learning with Feature-Dependent Label Noise: A Progressive Approach. [Paper][Code]
- Poster Session 9: May 5, 2021, 5 p.m. -- May 5, 2021, 7 p.m. (PDT)
Robust early-learning: Hindering the memorization of noisy labels. [Paper][Code]
- Poster Session 11: May 6, 2021, 9 a.m. -- May 6, 2021, 11 a.m. (PDT)
Robust Curriculum Learning: from clean label detection to noisy label self-correction. [Paper]
- Poster Session 3: May 3, 2021, 5 p.m. -- May 3, 2021, 7 p.m. (PDT)
How Does Mixup Help With Robustness and Generalization? [Paper]
- Poster Session 8: May 5, 2021, 9 a.m. -- May 5, 2021, 11 a.m. (PDT)
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data. [Paper]
- Poster Session 12: May 6, 2021, 5 p.m. -- May 6, 2021, 7 p.m. (PDT)

CVPR 2021

Conference date: Jun 19, 2021 -- Jun 25, 2021

[UCSC REAL Lab] A Second-Order Approach to Learning with Instance-Dependent Label Noise. [Paper][Code]
Improving Unsupervised Image Clustering With Robust Learning. [Paper]
Multi-Objective Interpolation Training for Robustness to Label Noise. [Paper][Code]
Noise-resistant Deep Metric Learning with Ranking-based Instance Selection. [Paper][Code]
Augmentation Strategies for Learning with Noisy Labels. [Paper][Code]
Jo-SRC: A Contrastive Approach for Combating Noisy Labels. [Paper][Code]
Multi-Objective Interpolation Training for Robustness to Label Noise. [Paper][Code]
AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation. [Paper][Code]
Meta Pseudo Labels. [Paper][Code]
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training. [Paper][Code]
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification. [Paper][Code]

AISTATS 2021

Conference date: Apr 13, 2021 -- Apr 15, 2021

Collaborative Classification from Noisy Labels. [Paper]
Linear Models are Robust Optimal Under Strategic Behavior. [Paper]

AAAI 2021

Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise. [Paper][Code]
Learning to Purify Noisy Labels via Meta Soft Label Corrector. [Paper][Code]
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels. [Paper][Code]
Learning from Noisy Labels with Complementary Loss Functions. [Paper][Code]
Analysing the Noise Model Error for Realistic Noisy Label Data. [Paper][Code]
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. [Paper]
Learning with Group Noise. [Paper]
Meta Label Correction for Noisy Label Learning. [Paper]

ArXiv 2021

[UCSC REAL Lab] The importance of understanding instance-level noisy labels. [Paper]
[UCSC REAL Lab] Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels. [Paper][Code]
A Theoretical Analysis of Learning with Noisily Labeled Data. [Paper]
A Survey of Label-noise Representation Learning: Past, Present and Future. [Paper]
Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization. [Paper][Code]
Noisy-Labeled NER with Confidence Estimation. [Paper][Code]
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels. [Paper][Code]
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels. [Paper][Code]
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond. [Paper]
Understanding the Interaction of Adversarial Training with Noisy Labels. [Paper]
Learning from Noisy Labels via Dynamic Loss Thresholding. [Paper]
Evaluating Multi-label Classifiers with Noisy Labels. [Paper]
Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptation. [Paper]
Transform consistency for learning with noisy labels. [Paper]
Learning to Combat Noisy Labels via Classification Margins. [Paper]
Joint Negative and Positive Learning for Noisy Labels. [Paper]
Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment. [Paper]
DST: Data Selection and joint Training for Learning with Noisy Labels. [Paper]
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment. [Paper]
A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels. [Paper]
Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction. [Paper]
MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels. [Paper]
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels. [Paper]
Co-matching: Combating Noisy Labels by Augmentation Anchoring. [Paper]
Pathological Image Segmentation with Noisy Labels. [Paper]
CrowdTeacher: Robust Co-teaching with Noisy Answers & Sample-specific Perturbations for Tabular Data. [Paper]
Friends and Foes in Learning from Noisy Labels. [Paper]
Learning from Noisy Labels for Entity-Centric Information Extraction. [Paper]
A Fremework Using Contrastive Learning for Classification with Noisy Labels. [Paper]
Contrastive Learning Improves Model Robustness Under Label Noise. [Paper][Code]

Papers & Code in 2020

ICML 2020

[UCSC REAL Lab] Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates. [Paper][Code 1] [Code 2]
Normalized Loss Functions for Deep Learning with Noisy Labels. [Paper][Code]
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. [Paper][Code]
Error-Bounded Correction of Noisy Labels. [Paper][Code]
Training Binary Neural Networks through Learning with Noisy Supervision. [Paper][Code]
Improving generalization by controlling label-noise information in neural network weights. [Paper][Code]
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training. [Paper][Code]
Searching to Exploit Memorization Effect in Learning with Noisy Labels. [Paper][Code]
Learning with Bounded Instance and Label-dependent Label Noise. [Paper]
Label-Noise Robust Domain Adaptation. [Paper]
Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels. [Paper]
Does label smoothing mitigate label noise?. [Paper]
Learning with Multiple Complementary Labels. [Paper]
Deep k-NN for Noisy Labels. [Paper]
Extreme Multi-label Classification from Aggregated Labels. [Paper]

ICLR 2020

DivideMix: Learning with Noisy Labels as Semi-supervised Learning. [Paper][Code]
Learning from Rules Generalizing Labeled Exemplars. [Paper] [Code]
Robust training with ensemble consensus. [Paper][Code]
Self-labelling via simultaneous clustering and representation learning. [Paper][Code]
Can gradient clipping mitigate label noise? [Paper][Code]
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification. [Paper][Code]
Curriculum Loss: Robust Learning and Generalization against Label Corruption. [Paper]
Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee. [Paper]
SELF: Learning to Filter Noisy Labels with Self-Ensembling. [Paper]

Nips 2020

Part-dependent Label Noise: Towards Instance-dependent Label Noise. [Paper][Code]
Identifying Mislabeled Data using the Area Under the Margin Ranking. [Paper][Code]
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning. [Paper]
Early-Learning Regularization Prevents Memorization of Noisy Labels. [Paper][Code]
Coresets for Robust Training of Deep Neural Networks against Noisy Labels. [Paper][Code]
Modeling Noisy Annotations for Crowd Counting. [Paper][Code]
Robust Optimization for Fairness with Noisy Protected Groups. [Paper][Code]
Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping. [Paper][Code]
A Topological Filter for Learning with Label Noise. [Paper][Code]
Self-Adaptive Training: beyond Empirical Risk Minimization. [Paper][Code]
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images. [Paper][Code]
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise. [Paper]
Efficient active learning of sparse halfspaces with arbitrary bounded noise. [Paper]
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. [Paper]
Labelling unlabelled videos from scratch with multi-modal self-supervision. [Paper][Code]
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning. [Paper][Code]
MetaPoison: Practical General-purpose Clean-label Data Poisoning. [Paper][Code 1][Code 2]
Provably Consistent Partial-Label Learning. [Paper]
A Variational Approach for Learning from Positive and Unlabeled Data. [Paper][Code]

AAAI 2020

[UCSC REAL Lab] Reinforcement Learning with Perturbed Rewards. [Paper] [Code]
Less Is Better: Unweighted Data Subsampling via Influence Function. [Paper] [Code]
Weakly Supervised Sequence Tagging from Noisy Rules. [Paper][Code]
Coupled-View Deep Classifier Learning from Multiple Noisy Annotators. [Paper]
Partial multi-label learning with noisy label identification. [Paper]
Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. [Paper]
Label Error Correction and Generation Through Label Relationships. [Paper]

CVPR 2020

Combating noisy labels by agreement: A joint training method with co-regularization. [Paper][Code]
Distilling Effective Supervision From Severe Label Noise. [Paper][Code]
Self-Training With Noisy Student Improves ImageNet Classification. [Paper][Code]
Noise Robust Generative Adversarial Networks. [Paper][Code]
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition. [Paper]
DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data. [Paper]
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss. [Paper][Code]
Training Noise-Robust Deep Neural Networks via Meta-Learning. [Paper][Code]
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data. [Paper][Code]
Noise-Aware Fully Webly Supervised Object Detection. [Paper][Code]
Learning From Noisy Anchors for One-Stage Object Detection. [Paper][Code]
Generating Accurate Pseudo-Labels in Semi-Supervised Learning and Avoiding Overconfident Predictions via Hermite Polynomial Activations. [Paper][Code]
Revisiting Knowledge Distillation via Label Smoothing Regularization. [Paper][Code]

ECCV 2020

2020-ECCV - Learning with Noisy Class Labels for Instance Segmentation. [Paper][Code]
2020-ECCV - Suppressing Mislabeled Data via Grouping and Self-Attention. [Paper][Code]
2020-ECCV - NoiseRank: Unsupervised Label Noise Reduction with Dependence Models. [Paper]
2020-ECCV - Weakly Supervised Learning with Side Information for Noisy Labeled Images. [Paper]
2020-ECCV - Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection. [Paper]
2020-ECCV - Graph convolutional networks for learning with few clean and many noisy labels. [Paper]

ArXiv 2020

No Regret Sample Selection with Noisy Labels. [Paper][Code]
Meta Soft Label Generation for Noisy Labels. [Paper][Code]
Learning from Noisy Labels with Deep Neural Networks: A Survey. [Paper]
RAR-U-Net: a Residual Encoder to Attention Decoder by Residual Connections Framework for Spine Segmentation under Noisy Labels. [Paper]
Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning Approach. [Paper]

1980x/Advances-in-Label-Noise-Learning