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Learning with noisy labels

Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data.

Papers

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Showing 1–50 of 249 papers

TitleStatusHype
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy—0
On the Role of Label Noise in the Feature Learning ProcessCode1
Detect and Correct: A Selective Noise Correction Method for Learning with Noisy LabelsCode0
Exploring Video-Based Driver Activity Recognition under Noisy LabelsCode0
Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization—0
Learning from Noisy Labels with Contrastive Co-Transformer—0
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples—0
Early Stopping Against Label Noise Without Validation DataCode0
Learning with Noisy Labels: the Exploration of Error Bounds in Classification—0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
Label Calibration in Source Free Domain Adaptation—0
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labelsCode0
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
NLPrompt: Noise-Label Prompt Learning for Vision-Language ModelsCode2
In-Context Learning with Noisy Labels—0
ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy LabelsCode0
May the Forgetting Be with You: Alternate Replay for Learning with Noisy LabelsCode0
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
NoisyAG-News: A Benchmark for Addressing Instance-Dependent Noise in Text Classification—0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Foster Adaptivity and Balance in Learning with Noisy LabelsCode0
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Benchmarking Label Noise in Instance Segmentation: Spatial Noise MattersCode0
Relation Modeling and Distillation for Learning with Noisy Labels—0
Jump-teaching: Ultra Efficient and Robust Learning with Noisy Label—0
High-dimensional Learning with Noisy Labels—0
Can We Treat Noisy Labels as Accurate?Code0
Exploring the Robustness of In-Context Learning with Noisy LabelsCode0
Cross-to-merge training with class balance strategy for learning with noisy labelsCode0
Deep learning with noisy labels in medical prediction problems: a scoping review—0
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Mitigating Label Noise on Graph via Topological Sample SelectionCode0
SURE: SUrvey REcipes for building reliable and robust deep networksCode2
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation LearningCode0
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection—0
Debiased Sample Selection for Combating Noisy LabelsCode0
Dirichlet-Based Prediction Calibration for Learning with Noisy LabelsCode1
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality GuidanceCode0
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations—0
Learning with Structural Labels for Learning with Noisy Labels—0
Robust Loss Functions for Training Decision Trees with Noisy LabelsCode0
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy LabelsCode0
A Unified Framework for Connecting Noise Modeling to Boost Noise DetectionCode0
Learning to Complement with Multiple Humans—0
Fine tuning Pre trained Models for Robustness Under Noisy Labels—0
Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning—0
Learning with Noisy Labels for Human Fall Events Classification: Joint Cooperative Training with Trinity Networks—0
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