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Found 4 papers, 3 papers with code
Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
In this paper, we attempt to query examples that are both likely from known classes and highly informative, and propose a \textit{Bidirectional Uncertainty-based Active Learning} (BUAL) framework.
Dirichlet-Based Prediction Calibration for Learning with Noisy Labels
Learning with noisy labels can significantly hinder the generalization performance of deep neural networks (DNNs).
Multi-Label Knowledge Distillation
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class single-label learning.
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.
