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Found 4 papers, 1 papers with code
Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise.
Noise-Robust Bidirectional Learning with Dynamic Sample Reweighting
Deep neural networks trained with standard cross-entropy loss are more prone to memorize noisy labels, which degrades their performance.
Learning from Crowds with Sparse and Imbalanced Annotations
Traditional supervised learning requires ground truth labels for the training data, whose collection can be difficult in many cases.
Multi-Label Active Learning from Crowds
Multi-label active learning is a hot topic in reducing the label cost by optimally choosing the most valuable instance to query its label from an oracle.
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