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About this paper
Thorben Werner, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi
The University of Hildesheim research systematically evaluates Active Learning (AL) alongside Data Augmentation (DA) and Semi-Supervised Learning (SSL) in modern machine learning pipelines. It quantifies that while DA and SSL provide substantial performance gains, AL can still offer marginal additional improvements when integrated into these optimized settings.
Key insights
- Active Learning, when used in isolation, provides significantly smaller performance gains (1-4% lift) at a much higher computational cost compared to Data Augmentation and Semi-Supervised Learning (up to 60% lift).
- Data Augmentation and Semi-Supervised Learning methods effectively complement each other, establishing a robust and strong baseline for handling limited labeled data.
- Even within highly optimized pipelines leveraging strong DA and SSL, Active Learning can still deliver statistically significant, albeit marginal, further performance enhancements.
Summary by alphaXiv.
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