5 citations · 5 across the 2 of their papers we have counts for
3 papers · 1 filter
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
Valentin Margraf, Marcel Wever, Sandra Gilhuber +3
In settings where only a budgeted amount of labeled data can be afforded, active learning seeks to devise query strategies for selecting the most informative data points to be labe…
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok +1
Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by…
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
Sandra Gilhuber, Julian Busch, Daniel Rotthues +2
Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data. To keep annotation costs low, active g…