Advances in Deep Active Learning and Synergies With Semi-Supervision

MCML Authors

Sandra Gilhuber (née Obermeier)

→ Group Thomas Seidl
 Database Systems, Data Mining and AI

Abstract

This thesis tackles the challenge of costly and limited annotated data for training deep learning models by advancing methods in deep active learning and its combination with semi-supervised learning. The work introduces efficient strategies to select informative and diverse samples without expensive computations, achieving superior performance in both tabular and image classification tasks. It further develops a novel approach for active learning on graphs, leveraging diffusion-based heuristics for node selection, and proposes refined pseudo-labeling techniques to mitigate confirmation bias in semi-supervised settings. Altogether, the contributions demonstrate how integrating active and semi-supervised learning can reduce labeling costs, improve efficiency, and address real-world data challenges. (Shortened.)

phdthesis

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Dissertation

Feb. 2025

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Authors

S. Gilhuber

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Links

DOI

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Research Area

 A3 | Computational Models