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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 249 papers

TitleStatusHype
Co-Learning Meets Stitch-Up for Noisy Multi-label Visual RecognitionCode1
Early-Learning Regularization Prevents Memorization of Noisy LabelsCode1
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in MoviesCode1
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy LabelsCode1
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
Neighborhood Collective Estimation for Noisy Label Identification and CorrectionCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image ClassificationCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Understanding Generalized Label Smoothing when Learning with Noisy LabelsCode1
Show:102550
← PrevPage 8 of 25Next →

No leaderboard results yet.