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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 151–200 of 249 papers

TitleStatusHype
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
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method—0
SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels—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
Labeling Chaos to Learning Harmony: Federated Learning with Noisy LabelsCode0
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels—0
To Aggregate or Not? Learning with Separate Noisy Labels—0
Communication-Efficient Robust Federated Learning with Noisy Labels—0
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages—0
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels—0
PENCIL: Deep Learning with Noisy Labels—0
Identifiability of Label Noise Transition Matrix—0
Learning with Neighbor Consistency for Noisy LabelsCode0
Do We Need to Penalize Variance of Losses for Learning with Label Noise?—0
PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
Learning to Rectify for Robust Learning with Noisy LabelsCode0
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels—0
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy LabelsCode0
Prototypical Classifier for Robust Class-Imbalanced Learning—0
Clean or Annotate: How to Spend a Limited Data Collection Budget—0
Robust Temporal Ensembling for Learning with Noisy Labels—0
Understanding Sharpness-Aware Minimization—0
Chameleon Sampling: Diverse and Pure Example Selection for Online Continual Learning with Noisy Labels—0
Co-variance: Tackling Noisy Labels with Sample Selection by Emphasizing High-variance Examples—0
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?—0
Relative Instance Credibility Inference for Learning with Noisy Labels—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
Confidence Adaptive Regularization for Deep Learning with Noisy Labels—0
Cooperative Learning for Noisy Supervision—0
An Instance-Dependent Simulation Framework for Learning with Label NoiseCode0
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered BeneficialCode0
Mitigating Memorization in Sample Selection for Learning with Noisy Labels—0
Distilling effective supervision for robust medical image segmentation with noisy labels—0
DualGraph: A Graph-Based Method for Reasoning About Label Noise—0
Influential Rank: A New Perspective of Post-training for Robust Model against Noisy Labels—0
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels—0
Joint Text and Label Generation for Spoken Language Understanding—0
Transform consistency for learning with noisy labels—0
Co-matching: Combating Noisy Labels by Augmentation Anchoring—0
On the Robustness of Monte Carlo Dropout Trained with Noisy Labels—0
Learning with Group Noise—0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
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