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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 176–200 of 249 papers

TitleStatusHype
Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection—0
Learning with Label Noise for Image Retrieval by Selecting Interactions—0
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method—0
Learning with Noisy Labels—0
Unified Robust Training for Graph NeuralNetworks against Label Noise—0
Clean or Annotate: How to Spend a Limited Data Collection Budget—0
Learning with Noisy Labels for Human Fall Events Classification: Joint Cooperative Training with Trinity Networks—0
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels—0
Learning with Noisy Labels for Sentence-level Sentiment Classification—0
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations—0
Learning with Noisy Labels over Imbalanced Subpopulations—0
Sample-wise Label Confidence Incorporation for Learning with Noisy Labels—0
[Re] Can gradient clipping mitigate label noise?Code0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
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
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy LabelsCode0
Cross-to-merge training with class balance strategy for learning with noisy labelsCode0
LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge IntegrationCode0
Partial Label Supervision for Agnostic Generative Noisy Label LearningCode0
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label EnvironmentCode0
Making Deep Neural Networks Robust to Label Noise: a Loss Correction ApproachCode0
How does Disagreement Help Generalization against Label Corruption?Code0
May the Forgetting Be with You: Alternate Replay for Learning with Noisy LabelsCode0
CLID-MU: Cross-Layer Information Divergence Based Meta Update Strategy for Learning with Noisy LabelsCode0
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