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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 76–100 of 249 papers

TitleStatusHype
Fine-Grained Classification with Noisy Labels—0
Latent Class-Conditional Noise ModelCode0
Learning with Noisy labels via Self-supervised Adversarial Noisy MaskingCode1
When Source-Free Domain Adaptation Meets Learning with Noisy Labels—0
Knockoffs-SPR: Clean Sample Selection in Learning with Noisy LabelsCode1
Sample-wise Label Confidence Incorporation for Learning with Noisy Labels—0
RankMatch: Fostering Confidence and Consistency in Learning with Noisy Labels—0
Combating Noisy Labels with Sample Selection by Mining High-Discrepancy Examples—0
How To Prevent the Continuous Damage of Noises To Model Training?—0
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal RetrievalCode1
OT-Filter: An Optimal Transport Filter for Learning With Noisy Labels—0
DISC: Learning From Noisy Labels via Dynamic Instance-Specific Selection and CorrectionCode1
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels—0
Model and Data Agreement for Learning with Noisy LabelsCode0
Dynamic Loss For Robust LearningCode0
Blind Knowledge Distillation for Robust Image ClassificationCode0
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels—0
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method—0
Learning with Noisy Labels over Imbalanced Subpopulations—0
Learning advisor networks for noisy image classificationCode0
Bootstrapping the Relationship Between Images and Their Clean and Noisy LabelsCode0
Semantic Segmentation with Active Semi-Supervised Representation Learning—0
Instance-Dependent Noisy Label Learning via Graphical ModellingCode1
Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence PenalizationCode1
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