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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 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
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative ModelCode1
MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial ImagesCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy LabelsCode1
Faster Meta Update Strategy for Noise-Robust Deep LearningCode1
Understanding Generalized Label Smoothing when Learning with Noisy LabelsCode1
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