5 citations · 5 across the 2 of their papers we have counts for
2 papers
cs.LG2023
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…
cs.LG2023★ 5 cited
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…