Real crowd-sourced text annotations are used to test eight active learning techniques, showing their behavior under actual noisy labeling and refusals unlike simulated oracles.
Neural active learning on heteroskedastic distributions
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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Active learning with foundation model priors achieves over 50% annotation savings on imbalanced noisy datasets across image and text domains while maintaining performance.
citing papers explorer
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An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
Real crowd-sourced text annotations are used to test eight active learning techniques, showing their behavior under actual noisy labeling and refusals unlike simulated oracles.
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Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
Active learning with foundation model priors achieves over 50% annotation savings on imbalanced noisy datasets across image and text domains while maintaining performance.