SOTAVerified

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

Showing 110 of 249 papers

TitleStatusHype
NLPrompt: Noise-Label Prompt Learning for Vision-Language ModelsCode2
SURE: SUrvey REcipes for building reliable and robust deep networksCode2
Sharpness-Aware Minimization for Efficiently Improving GeneralizationCode2
On the Role of Label Noise in the Feature Learning ProcessCode1
Active Negative Loss: A Robust Framework for Learning with Noisy LabelsCode1
CLIPCleaner: Cleaning Noisy Labels with CLIPCode1
AlleNoise: large-scale text classification benchmark dataset with real-world label noiseCode1
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label LearningCode1
Dirichlet-Based Prediction Calibration for Learning with Noisy LabelsCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
Show:102550
← PrevPage 1 of 25Next →

No leaderboard results yet.