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USE: Uncertainty Structure Estimation for Robust Semi-Supervised Learning
arXiv – CS AI|Tsao-Lun Chen, Chien-Liang Liu, Tzu-Ming Harry Hsu, Tai-Hsien Wu, Chi-Cheng Fu, Han-Yi E. Chou, Shun-Feng Su||1 views
🤖AI Summary
Researchers introduce Uncertainty Structure Estimation (USE), a new preprocessing method for semi-supervised learning that improves model reliability by filtering out low-quality unlabeled data. The approach uses entropy scores and statistical thresholds to identify and remove out-of-distribution samples before training, demonstrating consistent accuracy improvements across imaging and NLP tasks.
Key Takeaways
- →USE is an algorithm-agnostic preprocessing method that assesses unlabeled data quality before semi-supervised learning training begins.
- →The approach uses entropy scores from a proxy model to identify and filter out uninformative or harmful out-of-distribution samples.
- →Extensive experiments on CIFAR-100 and Yelp Review datasets show consistent accuracy and robustness improvements.
- →The method addresses a key bottleneck in semi-supervised learning by focusing on data quality rather than algorithmic design.
- →USE enables more reliable deployment of semi-supervised learning in realistic mixed-distribution environments.
#machine-learning#semi-supervised-learning#data-quality#uncertainty-estimation#out-of-distribution#preprocessing#robustness#arxiv
Read Original →via arXiv – CS AI
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